Implantable seizure monitor

ABSTRACT

An implantable seizure monitor can include at least one sensing electrode and an electronics module configured to detect, record and/or log neurological events. For example, the electronics module can be configured to detect brainwaves indicative of seizures, such as, for example, epileptic seizures, and to create a log indicating when such seizures occur. The implantable seizure monitor can include a cushioning member made of a soft material and configured to be implantable between the epidermis and cranium of a patient.

RELATED APPLICATION

This application is a continuation application of U.S. patentapplication Ser. No. 11/371,701, entitled “Implantable Seizure Monitor”filed Mar. 8, 2006, which is incorporated by reference herein in itsentirety.

BACKGROUND OF THE INVENTIONS

1. Field of the Inventions

The inventions disclosed herein are directed to devices for detectingbrainwaves, and more particularly, devices for detecting and loggingneurological events indicative of seizures.

2. Description of the Related Art

Epilepsy, a neurological disorder characterized by the occurrence ofseizures (specifically episodic impairment or loss of consciousness,abnormal motor phenomena, psychic or sensory disturbances, or theperturbation of the autonomic nervous system), is debilitating to agreat number of people. It is believed that as many as two to fourmillion Americans may suffer from various forms of epilepsy. Researchhas found that its prevalence may be even greater worldwide,particularly in less economically developed nations, suggesting that theworldwide figures for epilepsy sufferers may be in excess of one hundredmillion.

Because epilepsy is characterized by seizures, its sufferers arefrequently limited in the kinds of activities they may participate in.Epilepsy can prevent people from driving, working, or otherwiseparticipating in much of what society has to offer. Some epilepsysufferers have serious seizures so frequently that they are effectivelyincapacitated.

Furthermore, epilepsy is often progressive and can be associated withdegenerative disorders and conditions. Over time, epileptic seizuresoften become more frequent and more serious, and in particularly severecases, are likely to lead to deterioration of other brain functions(including cognitive function) as well as physical impairments.

The current state of the art in treating neurological disorders,particularly epilepsy, typically involves drug therapy and surgery. Thefirst approach is usually drug therapy.

A number of drugs are approved and available for treating epilepsy, suchas sodium valproate, phenobarbital/primidone, ethosuximide, gabapentin,phenytoin, and carbamazepine, as well as a number of others.Unfortunately, those drugs typically have serious side effects,especially toxicity, and it is extremely important in most cases tomaintain a precise therapeutic serum level to avoid breakthroughseizures (if the dosage is too low) or toxic effects (if the dosage istoo high). The need for patient discipline is high, especially when apatient's drug regimen causes unpleasant side effects the patient maywish to avoid.

Moreover, while many patients respond well to drug therapy alone, asignificant number (at least 20-30%) do not. For those patients, surgeryis presently the best-established and most viable alternative course oftreatment.

Currently practiced surgical approaches include radical surgicalresection such as hemispherectomy, corticectomy, lobectomy and partiallobectomy, and less-radical lesionectomy, transection, and stereotacticablation. Besides being less than fully successful, these surgicalapproaches generally have a high risk of complications, and can oftenresult in damage to eloquent (i.e., functionally important) brainregions and the consequent long-term impairment of various cognitive andother neurological functions. Furthermore, for a variety of reasons,such surgical treatments are contraindicated in a substantial number ofpatients. And unfortunately, even after radical brain surgery, manyepilepsy patients are still not seizure-free.

Electrical stimulation is an emerging therapy for treating epilepsy.However, currently approved and available electrical stimulation devicesapply continuous electrical stimulation to neural tissue surrounding ornear implanted electrodes, and do not perform any detection—they are notresponsive to relevant neurological conditions.

The NeuroCybernetic Prosthesis (NCP) from Cyberonics, for example,applies continuous electrical stimulation to the patient's vagus nerve.This approach has been found to reduce seizures by about 50% in about50% of patients. Unfortunately, a much greater reduction in theincidence of seizures is needed to provide clinical benefit. Medtronicpresently offers several DBS systems, including the Activa, Solectra,and Kinetra systems. The Activa system includes a pectorally implantedcontinuous deep brain stimulator intended primarily to treat Parkinson'sdisease. In operation, it supplies a continuous electrical pulse streamto a selected deep brain structure where an electrode has beenimplanted.

Continuous stimulation of deep brain structures for the treatment ofepilepsy has not met with consistent success. To be effective interminating seizures, it is believed that one effective site wherestimulation should be performed is near the focus of the epileptogenicregion. The focus is often in the neocortex, where continuousstimulation may cause significant neurological deficit with clinicalsymptoms including loss of speech, sensory disorders, or involuntarymotion. Accordingly, research has been directed toward automaticresponsive epilepsy treatment based on a detection of imminent seizure.

A typical epilepsy patient experiences episodic attacks or seizures,which are generally electrographically defined as periods of abnormalneurological activity. As is traditional in the art, such periods shallbe referred to herein as “ictal”.

Most prior work on the detection and responsive treatment of seizuresvia electrical stimulation has focused on analysis ofelectroencephalogram (EEG) and electrocorticogram (ECoG) waveforms. Ingeneral, EEG signals represent aggregate neuronal activity potentialsdetectable via electrodes applied to a patient's scalp. ECoG signals,deep-brain counterparts to EEG signals, are detectable via electrodesimplanted on or under the dura mater, and usually within the patient'sbrain. Unless the context clearly and expressly indicates otherwise, theterm “EEG” shall be used generically herein to refer to both EEG andECoG signals.

Much of the work on detection has focused on the use of time-domainanalysis of EEG signals. See, e.g., J. Gotman, Automatic seizuredetection: improvements and evaluation, Electroencephalogr. Clin.Neurophysiol. 1990; 76(4): 317-24. In a typical time-domain detectionsystem, EEG signals are received by one or more implanted electrodes andthen processed by a control module, which then is capable of performingan action (intervention, warning, recording, etc.) when an abnormalevent is detected.

It is generally preferable to be able to detect and treat a seizure ator near its beginning, or even before it begins. The beginning of aseizure is referred to herein as an “onset.” However, it is important tonote that there are two general varieties of seizure onsets. A “clinicalonset” represents the beginning of a seizure as manifested throughobservable clinical symptoms, such as involuntary muscle movements orneurophysiological effects such as lack of responsiveness. An“electrographic onset” refers to the beginning of detectableelectrographic activity indicative of a seizure. An electrographic onsetwill frequently occur before the corresponding clinical onset, enablingintervention before the patient suffers symptoms, but that is not alwaysthe case. In addition, there are changes in the EEG that occur secondsor even minutes before the electrographic onset that can be identifiedand used to facilitate intervention before electrographic or clinicalonsets occur. This capability would be considered seizure prediction, incontrast to the detection of a seizure or its onset.

In the Gotman system, EEG waveforms are filtered and decomposed into“features” representing characteristics of interest in the waveforms.One such feature is characterized by the regular occurrence (i.e.,density) of half-waves exceeding a threshold amplitude occurring in aspecified frequency band between approximately 3 Hz and 20 Hz,especially in comparison to background (non-ictal) activity. When suchhalf-waves are detected, it is believed that seizure activity isoccurring For related approaches, see also H. Qu and J. Gotman, Aseizure warning system for long term epilepsy monitoring, Neurology1995; 45: 2250-4; and H. Qu and J. Gotman, A Patient-Specific Algorithmfor the Detection of Seizure Onset in Long-Term EEG Monitoring: PossibleUse as a Warning Device, IEEE Trans. Biomed. Eng. 1997; 44(2): 115-22.

The Gotman articles address half wave characteristics in general, andintroduce a variety of measurement criteria, including a ratio ofcurrent epoch amplitude to background; average current epoch EEGfrequency; average background EEG frequency; coefficient of variation ofwave duration; ratio of current epoch amplitude to following timeperiod; average wave amplitude; average wave duration; dominantfrequency (peak frequency of the dominant peak); and average power in amain energy zone. These criteria are variously mapped into ann-dimensional space, and whether a seizure is detected depends on thevector distance between the parameters of a measured segment of EEG anda seizure template in that space.

It should be noted that the schemes set forth in the above articles arenot tailored for use in an implantable device, and hence typicallyrequire more computational ability than would be available in such adevice.

U.S. Pat. No. 6,018,682 to Rise describes an implantable seizure warningsystem that implements a form of the Gotman system. However, the systemdescribed therein uses only a single detection modality, namely a countof sharp spike and wave patterns within a timer period. This isaccomplished with relatively complex processing, including averagingover time and quantifying sharpness by way of a second derivative of thesignal. The Rise patent does not disclose how the signals are processedat a low level, nor does it explain detection criteria in anysufficiently specific level of detail.

A more computationally demanding approach is to transform EEG signalsinto the frequency domain for rigorous spectrum analysis. See, e.g.,U.S. Pat. No. 5,995,868 to Dorfineister et al., which analyzes the powerspectral density of EEG signals in comparison to backgroundcharacteristics. Although this approach is generally believed to achievegood results, for the most part, its computational expense renders itless than optimal for use in long-term implanted epilepsy monitor andtreatment devices. With current technology, the battery life in animplantable device computationally capable of performing theDorfineister method would be too short for it to be feasible.

Also representing an alternative and more complex approach is U.S. Pat.No. 5,857,978 to Hively et al., in which various non-linear andstatistical characteristics of EEG signals are analyzed to identify theonset of ictal activity. Once more, the calculation of statisticallyrelevant characteristics is not believed to be feasible in animplantable device.

U.S. Pat. No. 6,016,449 to Fischell, et al. (which is herebyincorporated by reference as though set forth in full herein), describesan implantable seizure detection and treatment system. In the Fischellet al. system, various detection methods are possible, all of whichessentially rely upon the analysis (either in the time domain or thefrequency domain) of processed EEG signals. Fischell's controller ispreferably implanted intracranially, but other approaches are alsopossible, including the use of an external controller. When a seizure isdetected, the Fischell system applies responsive electrical stimulationto terminate the seizure, a capability that will be discussed in furtherdetail below.

All of these approaches provide useful information, and in some casesmay provide sufficient information for accurate detection and predictionof most imminent epileptic seizures.

However, none of the various implementations of the known approachesprovide 100% seizure detection accuracy in a clinical environment.

Two types of detection errors are generally possible. A “falsepositive,” as the term is used herein, refers to a detection of aseizure or ictal activity when no seizure or other abnormal event isactually occurring. Similarly, a “false negative” herein refers to thefailure to detect a seizure or ictal activity that actually is occurringor shortly will occur.

In most cases, with all known implementations of the known approachesfor detecting abnormal seizure activity solely by monitoring andanalyzing EEG activity, when a seizure detection algorithm is tuned tocatch all seizures, there will be a significant number of falsepositives. While it is currently believed that there are minimal or noside effects to limited amounts of over-stimulation (e.g., providingstimulation sufficient to terminate a seizure in response to a falsepositive), the possibility of accidentally initiating a seizure orincreasing the patient's susceptibility to seizures must be considered.

As is well known, it has been suggested that it is possible to treat andterminate seizures by applying electrical stimulation to the brain. See,e.g., U.S. Pat. No. 6,016,449 to Fischell et al., and H. R. Wagner, etal., Suppression of cortical epileptiform activity by generalized andlocalized ECoG desynchronization, Electroencephalogr. Clin.Neurophysiol. 1975; 39(5): 499-506. And as stated above, it is believedto be beneficial to perform this stimulation only when a seizure (orother undesired neurological event) is occurring or about to occur, asinappropriate stimulation may result in the initiation of seizures.

Furthermore, it should be noted that a false negative (that is, aseizure that occurs without any warning or treatment from the device)will often cause the patient significant discomfort and detriment.Clearly, false negatives are to be avoided.

It has been found to be difficult to achieve an acceptably low level offalse positives and false negatives with the level of computationalability available in an implantable device with reasonable battery life.

Preferably, the battery in an implantable device, particularly oneimplanted intracranially, should last at least several years. There is asubstantial risk of complications (such as infection, blood clots, andthe overgrowth of scar tissue) and lead failure each time an implanteddevice or its battery is replaced.

As stated above, the detection and prediction of ictal activity hastraditionally required a significant amount of computational ability.Moreover, for an implanted device to have significant real-worldutility, it is also advantageous to include a number of other featuresand capabilities. Specifically, treatment (via electrical stimulation ordrug infusion) and/or warning (via an audio annunciator, for example),recording of EEG signals for later consideration and analysis, andtelemetry providing a link to external equipment are all usefulcapabilities for an implanted device capable of detecting or predictingepileptiform signals. All of these additional subsystems will consumefurther power.

Moreover, size is also a consideration. For various reasons,intracranial implants are favored. A device implanted intracranially (orunder the scalp) will typically have a lower risk of failure than asimilar device implanted pectorally or elsewhere, which require a leadto be run from the device, through the patient's neck to the electrodeimplantation sites in the patient's head. This lead is also prone toreceive additional electromagnetic interference.

As is well known in the art, the computational ability of aprocessor-controlled system is directly related to both size and powerconsumption. In accordance with the above considerations, therefore, itwould be advantageous to have sufficient detection and predictioncapabilities to avoid a substantial number of false positive and falsenegative detections, and yet consume little enough power (in conjunctionwith the other subsystems) to enable long battery life. Such animplantable device would have a relatively low-power central processingunit to reduce the electrical power consumed by that portion.

More recently, as described in U.S. Pat. No. 6,810,285, issued to Plesset al., implantable devices have been developed which provide bothdetection/prediction of ictal activity and electrostimulation forattenuating or stopping an epileptic seizure. These devices areimplanted firstly by performing a craniotomy in which a portion of theskull is cut away and then mounting the device in the empty space leftafter the craniotomy. Electrodes that are connected to the implantdevice are implanted onto the surface of or into the brain lobes at adepth of up to about 1 to 2 cm. These electrodes are used for thedetection of ictal activity as well as the delivery ofelectrostimulation.

In order to identify the preferred implantation sites for theelectrodes, it is advantageous to record a patient's brainwaves, andmore particularly, brainwaves indicative of ictal activity, to identifythe area from which the ictal brainwaves originate. Further, it isadvantageous to identify the frequency of a patient's ictal activity inorder to optimize the device that is eventually implanted into thepatient's brain matter.

One persistent hurdle that remains in the precise diagnosis andunderstanding of a particular patient's form of epilepsy is thepatient's memory regarding his or her own seizure activity. For example,an epileptic patient can have seizures while they are awake, yetcompletely forget the seizure ever occurred. Further, epileptic patientscan also have seizures during sleep, and thus, never have an opportunityto form a memory of the seizure.

SUMMARY OF THE INVENTIONS

An aspect of at least one of the embodiments disclosed herein includesthe realization that the diagnosis and the adaptation of a treatment foran epileptic patient can be enhanced by providing a patient with animplantable seizure monitor that is, for example, configured to detectand/or record brainwaves indicative of ictal activity.

In some embodiments, the device can be implanted between the epidermisand the skull. As such, a craniotomy is not required to implant thedevice, thus reducing the complexity of the implantation procedure andreducing the risks of surgery. Further, the patient can have the benefitof objective and reliable recording and/or logging of ictal eventswithout the need for external wire leads which may be inconvenient orembarrassing for a patient.

In some embodiments, the device can include a processor and a powersupply mounted within a housing and a cushioning material in which thehousing is suspended. Sensors for detecting brainwaves can be suspendedin the cushioning material and connected to the housing with leads. Assuch, the device can be comfortably implanted between the epidermis andthe cranium. Additionally, this arrangement allows the sensors to bespaced apart to provide better directional detection of the ictalbrainwaves and allows the size of the housing to be reduced.

Thus, in accordance with an aspect of at least one of the embodimentsdisclosed herein, an implantable recording device for detecting andlogging neurological events is provided. The recording device caninclude a housing enclosing at least one electronic module and at leasta first sensing electrode connected to the electronic module.Additionally, the recording device can comprise a cushioning membersurrounding at least three sides of each of the housing and the at leastone sensing electrode, the cushioning member being made from a softbiocompatible material.

In accordance with an aspect of at least another of the embodimentsdisclosed herein, a method of monitoring a seizure disorder of an animalis provided. The method can include implanting a seizure monitor betweenthe epidermis and cranium of the animal, wherein the seizure monitor caninclude a housing enclosing an electronic module configured to detectand log seizure events, at least first and second sensing electrodes,and the cushioning member surrounding at least three sides of each ofthe housing and the sensing electrodes.

In accordance with an aspect of at least a further embodiment, animplantable device for detecting neurological events can be provided.The device can comprise at least one sensor configured to detectbrainwaves and to generate a signal indicative of the brainwaves. Anamplifier can be configured to amplify signal from the sensor.Additionally, an event detector can be configured to determine if aneurological event has occurred based on the saturation of theamplifier.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features, and advantages of the inventions willbecome apparent from the detailed description below and the accompanyingdrawings, in which:

FIG. 1 is a schematic illustration of a patient's head showing theplacement of an implantable recording device according to an embodiment;

FIG. 2 is a schematic illustration of the placement of the recordingdevice of FIG. 1 between the epidermis and the skull of the patient;

FIG. 2 a is a schematic illustration of another optional placement ofthe recording device of FIG. 1;

FIG. 3 is a block diagram illustrating an environment of use in whichthe implantable recording device, in accordance with an embodiment, isimplanted and operated;

FIG. 4 is a block diagram illustrating the functional subsystems of animplantable recording device according to an embodiment;

FIG. 5 is a block diagram illustrating the functional components of thedetection subsystem of the implantable recording device shown in FIG. 4;

FIG. 6 is a block diagram illustrating the functional components of thesensing front end of the detection subsystem of FIG. 5;

FIG. 7 is a block diagram illustrating the components of the waveformanalyzer of the detection subsystem of FIG. 5;

FIG. 8 is a block diagram illustrating the functional arrangement ofcomponents of the waveform analysis of the detection subsystem of FIG. 5in a possible programmed embodiment;

FIG. 9 is a graph of an exemplary EEG signal, illustrating decompositionof the signal into time windows and samples;

FIG. 10 is a graph of the exemplary EEG signal of FIG. 9, illustratingthe extraction of half waves from the signal;

FIG. 11 is a flow chart illustrating the process performed by hardwarefunctional components of the waveform analyzer of FIG. 7 in extractinghalf waves as illustrated in FIG. 10;

FIG. 12 is a flow chart illustrating the process performed by softwarein the central processing unit in extracting and analyzing half wavesfrom an EEG signal;

FIG. 13 is a flow chart illustrating the process performed by softwarein the central processing unit in the application of an X of Y criterionto half wave windows;

FIG. 14 is a graph of the exemplary EEG signal of FIG. 9, illustratingthe calculation of a line length function;

FIG. 15 is a flow chart illustrating the process performed by hardwarefunctional components of the waveform analyzer of FIG. 7 in calculatingthe line length function as illustrated in FIG. 14;

FIG. 16 is a flow chart illustrating the process performed by softwarein the central processing unit in calculating and analyzing the linelength function of an EEG signal;

FIG. 17 is a graph of the exemplary EEG signal of FIG. 9, illustratingthe calculation of an area function;

FIG. 18 is a flow chart illustrating the process performed by hardwarefunctional components of the waveform analyzer of FIG. 7 in calculatingthe area function as illustrated in FIG. 17;

FIG. 19 is a flow chart illustrating the process performed by softwarein the central processing unit in calculating and analyzing the areafunction of an EEG signal;

FIG. 20 is a flow chart illustrating the process performed byevent-driven software in the central processing unit to analyze halfwave, line length, and area information for detection according to anembodiment;

FIG. 21 is a flow chart illustrating the combination of analysis toolsinto detection channels in an embodiment; and

FIG. 22 is a flow chart illustrating the combination of detectionchannels into event detectors in an embodiment;

FIG. 23 is an exemplary log of data indicative of the occurrence ofneurological events that can be used in conjunction with the recordingdevices disclosed herein;

FIG. 23A is an exemplary seizure report that can be generated based oninformation gathered by any of the implantable recording devicesillustrated in these figures;

FIG. 24 is a schematic top plan view of an exemplary but nonlimitingembodiment of the implantable recording device having a housing and atleast one sensor suspended in a cushioning material;

FIG. 25 is a side elevational view of the implantable recording deviceillustrated in FIG. 23; and

FIG. 26 is a left side elevational view of the implantable recordingdevice of FIG. 23;

FIG. 27 is a schematic top plan view of a modification of theimplantable recording device of FIG. 24;

FIG. 28 is a side elevational view of the implantable recording deviceillustrated in FIG. 27;

FIG. 29 is a left side elevational view of the implantable recordingdevice of FIG. 27;

FIG. 30 is a schematic side elevational view and partial sectional viewof a patient's skull in which the implantable recording device isinstalled;

FIG. 31 is a top plan schematic view of a patient's skull in which theimplantable recording device of FIG. 27 is installed;

FIG. 32 is a schematic representation of a patient's brainwaves andillustrates various stages of adjustment of an amplifier with in theimplantable recording device;

FIG. 33 is a schematic illustration of a modification of a waveformanalyzer that can be included in any of the implantable recordingdevices illustrated in the above figures.

FIG. 34 is an exemplary depiction of a patient's brainwaves detected bya detection device and including an extended period during which anamplifier in the detection device is saturated.

FIG. 35 is another exemplary depiction of a patient's brainwavesdetected by a detection device and including only isolated events duringwhich an amplifier in the detection device is saturated.

FIG. 36 includes, in an upper portion, the depiction of a patient'sbrainwaves from FIG. 34, broken down into windows, and a table, in alower portion, tabulating saturation count criteria for each window.

DETAILED DESCRIPTION OF THE INVENTIONS

The inventions described herein, with reference to detailed illustrativeand exemplary embodiments, are described in the context of an implantdisposed between the epidermis and skull of a human patient. However,the inventions disclosed herein can be used in other context as well. Itis apparent from the description provided below that the systems,apparatuses, and methods can be embodied in a wide variety of forms.Consequently, the specific structural and functional details disclosedherein are only representative and do not limit the scope of theinventions.

FIG. 1 depicts an implantable recording device 110 implanted in apatient 112, according to an embodiment. In this embodiment, theimplantable recording device 110 comprises a small self-containedbrainwave detecting device. As the term is used herein, a brainwavedetecting or recording device is a device capable of detecting orpredicting ictal activity (or other neurological events) for providingdata useful in the diagnosis of a neurological disorder. Further, theterm recording device, as used herein, is a device that can eitherrecord neurological signals, such as EEG signals, or detect and analyzeEEG signals and create a log of such an analysis.

In some embodiments, the implantable recording device 110 is configuredto be capable of detecting or predicting any kind of neurological eventthat has a representative electrographic signature. While the disclosedembodiment is described primarily as responsive to epileptic seizures,it should be recognized that it is also possible to respond to othertypes of neurological disorders, such as movement disorders (e.g. thetremors characterizing Parkinson's disease), migraine headaches, chronicpain, and neuropsychiatric disorders such as depression. Preferably,neurological events representing any or all of these afflictions can bedetected when they are actually occurring, in an onset stage, or as apredictive precursor before clinical symptoms begin.

In the disclosed embodiment, the recording device 110 is implantedbetween a patient's epidermis and skull, for example, as schematicallyillustrated in FIG. 2. It should be noted, however, that the placementdescribed and illustrated herein is merely exemplary, and otherlocations and configurations are also possible, depending on the sizeand shape of the device and individual patient needs, among otherfactors.

The device 110 is preferably configured to generally follow or to besufficiently deformable so as to follow the contours of a patient'scranium 214. However, other locations are also possible. For example,the device 110 can be configured to be implanted pectorally (not shown)with leads extending through the patient's neck and between thepatient's cranium and epidermis.

It should be recognized that the embodiment of the device 110 describedand illustrated herein is preferably a recording device for detectingseizures or their onsets or precursors, and recording or logging theseevents. For example, the device 110 can be configured to begin recordingall or some of the detected EEG signals from the patient at the onset oras a result of a prediction of ictal activity and to continue recordinguntil the ictal activity stops, and optionally, to save such arecording, or a sampling of it, to a memory device for laterdownloading. Alternatively, or in addition, the device 110 can beconfigured to create a log of such events.

For example, but without limitation, the device 110 can be configured torecord or log the date and time when each such event begins and ends,the duration of the event, indications of the intensity of the event,etc. The device 110 can also be configured, optionally, to download sucha log to external equipment, described in greater detail below.

With continued reference to FIG. 2, the device 110 can include housing226 configured to encapsulate an electronic module that is configured todetect and/or record the desired neurological signals. Additionally, thedevice 110 can include at least one sensor or sensing electrode 222configured to be sensitive to electronic neurological signals. Forexample, but without limitation, the sensor 222 can be formed from aplatinum member, or any other type of suitable material. The sensor 222can be incorporated into the housing 226 or can be connected to theelectronics within the housing with a lead implanted in or on the brainor upon the dura at the location of seizure onset so that the devicedoes not need to be located at the seizure onset focus. A separate leadcan be used if the seizure onset location was in an area of the brainwhere the housing could not be implanted due to surgical constraints. Aseparate lead can also be an option in the event that there are twoseizure foci in disparate locations and only one seizure focus would beapparent to a sensor incorporated into the housing.

The device 110 can also include a cushioning member 228 configured toprovide a comfortable cushion for the patient. For example, thecushioning member 228 can be comprised of silicone rubber or other typesof biocompatible material that can provide a comfortable cushion for adevice that is implanted between the epidermis and the cranium 214.

In some embodiments, the housing 226 is suspended completely within thecushioning member 228 such that no portion of the outer surface of thehousing 226 is exposed to the outer surface of the cushioning member228. In other embodiments, the housing 226 can be partially suspendedwithin the cushioning member 228 such that one or more surfaces of thehousing 226 are exposed to the outer surface of the cushioning member228.

In some embodiments, the sensor 222 is exposed to an outer surface ofthe cushioning member 228 so as to allow the sensor 222 to receiveneurological signals from the patient with as little attenuation aspossible.

To implant the device 110, firstly, an implantation site is chosen.Because the device 110 can be made quite small, it may be possible tofind a cranial contour having a somewhat recessed shape that is also inan acceptable place on the cranium that would serve as an appropriatelocation for recording ictal brainwaves. A small linear incision can bemade at this location with a length that is at least slightly largerthan the width of the device. A surgical instrument can be used toelevate the epidermis to form a pocket. In some embodiments, the device110 can be disposed below the dermis, the subcutaneous tissue, or thegalea.

For example, as shown in FIG. 2 a, the device 110 is illustrated asbeing disposed beneath the scalp, which comprises the epidermis, dermis,and subcutaneous tissue, and between the galea and the cranium 214. Insuch an embodiment, the small linear incision is made through the scalpand the galea.

With the epidermis, and/or dermis, and/or subcutaneous tissue, and/orthe galea, elevated with a surgical instrument, the device 110 can thenbe pushed into the space and thus form a pocket around the device 110.For this procedure, local anesthesia can be used and the entireprocedure can be completed on the order of 15 minutes. As such, thesurgical risks can be reduced.

In some variations, before the device 110 is inserted below theepidermis, dermis, subcutaneous tissue, or galea, an additional recesscan be drilled into the cranium 214. For example, a small recess (notshown) can be drilled into the cranium 214 for receiving this sensor222. As such, the sensor 222 can receive neurological signals with lessattenuation because there would be less bone between the sensor 222 andthe brain tissue that generates the electrographic activity that isdetected. Therefore, the signals would be larger in amplitude whichwould allow clearer reception for the sensor that would make thedetection process easier. And in other alternatives, the craniotomy canbe performed where the bone would be completely removed to allow evenclearer reception for the sensors 412.

With continued reference to FIG. 2, in yet other alternativesillustrated in phantom, a burr hole 215 can be drilled deeply into thecranium 214 or through the cranium 214. This provides yet clearerreception for the sensor 222. In such alternatives, the sensor 222 canbe pressed into such a burr hole, through the cranium 214, and/or intothe dura or cortex below the hole. The recess or hole can be sealedafter implantation to prevent further movement of the sensor 222 and/orits lead wire 223. For example, U.S. Pat. No. 6,006,124 issued toFishell et al., which is hereby incorporated by reference as though setforth in full herein, describes such a sealing method.

In other alternatives, the housing 226 can be implanted in anappropriate location of the cranium 214 and a separate lead can be usedto connect the electronics within the housing 226 with a sensor 222implanted in a seizure onset location that is more remote from thelocation of the housing.

The housing 226 can be fabricated from a biocompatible material. Forexample, but without limitation, Titanium, which is light, extremelystrong, and biocompatible, can be used to form the housing 226.

The housing 226 can enclose a battery and any electronic circuitry,described below in greater detail, to provide the functionalitydescribed herein, as well as any other features. As is described infurther detail below, a telemetry coil (not shown) can be providedinside or outside of the housing 226 (and potentially integrated with alead wire connecting the sensor 222 to the housing 226) to facilitatecommunication between the device 110 and external devices.

The implantable recording device 110 configuration described herein andillustrated in FIGS. 2 and 2 a provide several advantages overalternative designs. Firstly, the reduced capabilities of the device 110allows the entire package to be made much smaller than previous devicesthat were configured for monitoring and stimulation. This allows thepatient to avoid the more risky and expensive craniotomy procedure usedfor presently available neurostimulator devices. Additionally, the smallsize of the implantable seizure monitor device 110 causes a minimum ofcosmetic disfigurement.

As noted above, and as illustrated in FIG. 3, the recording device 110can operate in conjunction with external equipment. The device 110 canbe mostly autonomous (particularly when performing its usual sensing,detection, and recording capabilities), but preferably includes theselectable part-time wireless link 310 to external equipment, such as aprogrammer 312.

In the disclosed embodiment, the wireless link 310 can be established bymoving a wand (or other apparatus) having communication capabilities andcoupled to the programmer 312 into range of the device 110. Theprogrammer 312 can then be used to manually control the operation of thedevice 110, as well as to transmit information to or receive informationfrom the device 110. Several specific capabilities and operationsperformed by the programmer 312 in conjunction with the device aredescribed in further detail below.

The programmer 312 can be configured to perform a number of advantageousoperations. For example, the programmer 312 can be configured to specifyand set variable parameters in the device 110 to adapt the function ofthe device 110 to meet the patient's needs, download or receive data(including but not limited to stored EEG waveforms, parameters, or logsof events detected) from the device 110 to the programmer 312, upload ortransmit program code and other information from the programmer 312 tothe device 110, or command the device 110 to perform specific actions orchange modes as desired by a physician operating the programmer 312. Tofacilitate these functions, the programmer 312 is adapted to receivephysician input 314 and provide physician output 316; data istransmitted between the programmer 312 and the device 110 over thewireless link 310.

The programmer 312 can be coupled via a communication link 318 to anetwork 320 such as the Internet. This allows any information downloadedfrom the device 110, as well as any program code or other information tobe uploaded to the device 110, to be stored in a database at one or moredata repository locations (which may include various servers andnetwork-connected programmers like the programmer 312). This would allowa patient (and the patient's physician) to have access to importantdata, including past treatment information and software updates,essentially anywhere in the world that there is a programmer (like theprogrammer 312) and a network connection.

The device 110 can also have a sensor (not shown) configured to detect amagnetic field. For example, such a sensor can be configured to betriggered by a magnet moved into the vicinity of the device 110 by thepatient or caregiver when the patient was experiencing clinical symptomsof a seizure or other significant neurological event. The device 110 canadditionally be configured to then store an electrocorticogram samplethat would be indicative of the seizure or neurological event. Thesemagnet triggered electrocorticograms could then be analyzed to programthe detection parameters.

An overall block diagram of the device 110 used for measurement,detection, and/or recording is illustrated in FIG. 4. Several subsystemscan be disposed within the housing 226 forming a control module 410. Thecontrol module can be configured to be coupled to at least one electrode412. In some embodiments, the control module 410 is configured to beconnected to a plurality of electrodes. In some embodiments, the controlmodule 410 is configured to be connected to two electrodes 412, 414, ormore electrodes (each of which may be connected to the control module410 via a lead) for sensing and detection.

The connection between the leads connecting the electrodes 412, 414, tothe control module 410 can be accomplished through a lead connector (notshown). Although two electrodes are shown in FIG. 4, it should berecognized that any number is possible. In fact, it is possible toemploy an embodiment that uses a single lead with at least twoelectrodes, or two leads each with a single electrode (or with a secondelectrode provided by a conductive exterior portion of the housing 226in one embodiment), although bipolar sensing between two closely spacedelectrodes on a lead is preferred to minimize common mode signalsincluding noise.

The electrodes 412, 414 can be connected to an electrode interface 420.Preferably, the electrode interface is capable of selecting eachelectrode as required for sensing; accordingly the electrode interfaceis coupled to a detection subsystem 422. The electrode interface canalso provide any other features, capabilities, or aspects, including butnot limited to amplification, isolation, and charge-balancing functions,that can be used for a proper interface with neurological tissue and notprovided by any other subsystem of the device 110.

The detection subsystem 422 can include an EEG analyzer function. TheEEG analyzer function can be adapted to receive EEG signals from theelectrodes 412, 414, through the electrode interface 420, and to processthose EEG signals to identify neurological activity indicative of aseizure, an onset of a seizure, or a precursor to a seizure.

One way to implement such EEG analysis functionality is disclosed indetail in U.S. Pat. No. 6,016,449 to Fischell et al., incorporated byreference above, and additional methods are described in detail below.The detection subsystem can optionally also contain further sensing anddetection capabilities, including but not limited to parameters derivedfrom other physiological conditions (such as electrophysiologicalparameters, temperature, blood pressure, etc.).

The control module 410 can also include a memory subsystem 426 and acentral processing unit (CPU) 428, which can take the form of amicrocontroller. The memory subsystem can be coupled to the detectionsubsystem 422 (e.g., for receiving and storing data representative ofsensed EEG signals and evoked responses) and the CPU 428, which cancontrol the operation of the memory subsystem 426. In addition to thememory subsystem 426, the CPU 428 can also be connected to the detectionsubsystem 422 for direct control thereof.

The control module 410 can also include a communication subsystem 430which can be coupled to the memory subsystem 426 and the CPU 428. Thecommunication subsystem 430 can be configured to enable communicationbetween the device 110 (FIG. 1) and the outside world, particularly theexternal programmer 312 (FIG. 3). As noted above, in some embodiments,the communication subsystem 430 can include a telemetry coil (which maybe situated outside of the housing 226) enabling transmission andreception of signals, to or from an external apparatus, via inductivecoupling. Alternative embodiments of the communication subsystem 430could use an antenna for an RF link or an audio transducer for an audiolink to the patient to provide indications of neurological events orsystem status, or other indications.

The control module 410 can include other subsystems. For example, thecontrol module 410 can include a power supply 432 and a clock supply434. The power supply 432 can be configured to supply the voltages andcurrents desired for each of the other subsystems. The clock supply 434can be configured to supply substantially all of the other subsystemswith any clock and/or timing signals desired for their operation.

It should be noted that while the memory subsystem 426 is illustrated inFIG. 4 as a separate functional subsystem, the other subsystems can alsobe configured to use various amounts of memory to perform the functionsdescribed herein, as well as other functions. Furthermore, while thecontrol module 410 is preferably a single physical unit contained withina single physical enclosure, namely the housing 226 (FIG. 2), it cancomprise a plurality of spacially separate units each performing asubset of the capabilities described above. Also, it should be notedthat the various functions and capabilities of the subsystems describedherein can be performed by electronic hardware (e.g., hard wiredmodules), computer software (or firmware), or a combination thereof. Thedivision of work between the CPU 428 and other functional subsystems canalso vary—the functional distinctions illustrated in FIG. 4 may notreflect the integration of functions in a real-world system or methodaccording to the embodiments disclosed herein.

Rounding out the subsystems in the control module 410 are a power supply432 and a clock supply 434. The power supply 432 supplies the voltagesand currents necessary for each of the other subsystems. The clocksupply 434 supplies substantially all of the other subsystems with anyclock and timing signals necessary for their operation.

FIG. 5 illustrates details of the detection subsystem 422 (FIG. 4).Inputs from the electrodes 412, 414 are on the left, and connections toother subsystems are on the right.

Signals received from the electrodes 412, 414 (as routed through theelectrode interface 420) are received in an electrode selector 510. Theelectrode selector 510 allows the device to select which electrodesshould be routed to which individual sensing channels of the detectionsubsystem 422, based on commands received through a control interface518 from the memory subsystem 426 or the CPU 428 (FIG. 4).

Preferably, each sensing channel of the detection subsystem 422 receivesa bipolar signal representative of the difference in electricalpotential between two selectable electrodes. Accordingly, the electrodeselector 510 provides signals corresponding to each pair of selectedelectrodes to a sensing front end 512, which performs amplification,analog to digital conversion, and multiplexing functions on the signalsin the sensing channels. The sensing front end is described furtherbelow in connection with FIG. 6. In some embodiments, where the device110 only includes two sensors (e.g., sensors 412, 414), the electrodeselector 510 can be eliminated, allowing the detection subsystem 422 tooperate only on a single channel. This provides an advantage of furtherreducing the size of the overall device 110.

A multiplexed input signal representative of all active sensing channelscan then be fed from the sensing front end 512 to a waveform analyzer514. The waveform analyzer 514 is preferably a special-purpose digitalsignal processor (DSP) adapted for use with the embodiment, or in analternative embodiment, can comprise a programmable general-purpose DSP.

In some embodiments, the waveform analyzer can have its own scratchpadmemory area 516 used for local storage of data and program variableswhen signal processing is being performed. In either case, the signalprocessor performs suitable measurement and detection methods describedgenerally above and in greater detail below. Any results from suchmethods, as well as any digitized signals intended for storagetransmission to external equipment, are passed to various othersubsystems of the control module 410, including the memory subsystem 426and the CPU 428 (FIG. 4) through a data interface 520. Similarly, thecontrol interface 518 allows the waveform analyzer 514 and the electrodeselector 510 to be in communication with the CPU 428.

With reference to FIG. 6, the sensing front end 512 (FIG. 5) isillustrated in further detail. As shown, the sensing front end includesa plurality of differential amplifier channels 610, each of whichreceives a selected pair of inputs from the electrode selector 510.

In some embodiments, each of the differential amplifier channels 610 isadapted to receive or to share inputs with one or more otherdifferential amplifier channels 610 without adversely affecting thesensing and detection capabilities of a system according to someembodiments. For clarity, only one channel is illustrated in FIG. 6, butit should be noted that any practical number of sensing channels may beemployed in a system according to some embodiments.

Each differential amplifier channel 610 feeds a corresponding analog todigital converter (ADC) 612. Preferably, the analog to digitalconverters 612 are separately programmable with respect to samplerates—in the disclosed embodiment, the ADCs 612 convert analog signalsinto 10-bit unsigned integer digital data streams at a sample rateselectable between 250 Hz and 500 Hz.

In several of the illustrations described below where waveforms areshown, sample rates of 250 Hz are typically used for simplicity.However, the inventions disclosed herein shall not be deemed to be solimited, and numerous sample rate and resolution options are possible,with tradeoffs known to individuals of ordinary skill in the art ofelectronic signal processing. The resulting digital signals are receivedby a multiplexer 614 that creates a single interleaved digital datastream representative of the data from all active sensing channels. Asdescribed in further detail below, not all of the sensing channels needto be used at one time, and it may in fact be advantageous in certaincircumstances to deactivate certain sensing channels to reduce the powerconsumed by a system according to some embodiments.

It should be noted that as illustrated and described herein, a “sensingchannel” is not necessarily a single physical or functional item thatcan be identified in any illustration. Rather, a sensing channel can beformed from the functional sequence of operations described herein, andparticularly represents a single electrical signal received from anypair or combination of electrodes, as preprocessed by a system accordingto some embodiments, in both analog and digital forms. See, e.g., U.S.patent application Ser. No. 09/517,797 to D. Fischell et al., filed onMar. 2, 2000 and entitled “Neurological Event Detection Using ProcessedDisplay Channel Based Algorithms and Devices Incorporating TheseProcedures,” which is hereby incorporated by reference as though setforth in full herein. At times (particularly after the multiplexer 614),multiple sensing channels are processed by the same physical andfunctional components of the system; notwithstanding that, it should berecognized that unless the description herein indicates to the contrary,a system according to some embodiments processes, handles, and treatseach sensing channel independently.

The interleaved digital data stream is passed from the multiplexer 614,out of the sensing front end 512, and into the waveform analyzer 514.The waveform analyzer 514 is illustrated in detail in FIG. 7.

The interleaved digital data stream representing information from all ofthe active sensing channels is first received by a channel controller710. The channel controller applies information from the active sensingchannels to a number of wave morphology analysis units 712 and windowanalysis units 714. It is preferred to have as many wave morphologyanalysis units 712 and window analysis units 714 as possible, consistentwith the goals of efficiency, size, and low power consumption necessaryfor an implantable device. In some embodiments, there are sixteen wavemorphology analysis units 712 and eight window analysis units 714, eachof which can receive data from any of the sensing channels of thesensing front end 512, and each of which can be operated with differentand independent parameters, including differing sample rates, as will bediscussed in further detail below.

Further, in some embodiments, such as embodiments using only a singlebipole channel, the wave form analyzer 514 can operate with as little asone or two wave morphology analysis units 712 and one or two windowanalysis units 714, each of which can receive data from the singlechannel of the sensing front end 512, each of which can be operated withdifferent and independent parameters, including different samplingrates. Reducing the number of wave morphology analysis units 712 andwindow analysis units 714 allows the recording device 110 to be furtherreduced in size.

Each of the wave morphology analysis units 712 can operate to extractcertain feature information from an input waveform as described below inconjunction with FIGS. 9-11. Similarly, each of the window analysisunits 714 can perform certain data reduction and signal analysis withintime windows in the manner described in conjunction with FIGS. 12-17.Output data from the various wave morphology analysis units 712 andwindow analysis units 714 can be combined via event detector logic 716.The event detector logic 716 and the channel controller 710 can becontrolled by control commands 718 received from the control interface518 (FIG. 5).

A “detection channel,” as the term is used herein, refers to a datastream including the active sensing front end 512 and the analysis unitsof the waveform analyzer 514 processing that data stream, in both analogand digital forms. It should be noted that each detection channel canreceive data from a single sensing channel; each sensing channelpreferably can be applied to the input of any combination of detectionchannels. The latter selection is accomplished by the channel controller710. As with the sensing channels, not all detection channels need to beactive; certain detection channels can be deactivated to save power orif additional detection processing is deemed unnecessary in certainapplications.

In conjunction with the operation of the wave morphology analysis units712 and the window analysis units 714, a scratchpad memory area 516 canbe provided for temporary storage of processed data. The scratchpadmemory area 516 can be physically part of the memory subsystem 426, oralternatively may be provided for the exclusive use of the waveformanalyzer 514. Other subsystems and components of a system according toan embodiment can also be furnished with local scratchpad memory, ifsuch a configuration is desired.

An exemplary but non-limiting operation of the event detector logic 716is illustrated in detail in the functional block diagram of FIG. 8, inwhich four exemplary sensing channels are analyzed by three illustrativeevent detectors.

A first sensing channel 810 provides input to a first event detector812. While the first event detector 812 is illustrated as a functionalblock in the block diagram of FIG. 8, it should be recognized that it isa functional block only for purposes of illustration, and may not haveany physical counterpart in a device according to some embodiments.Similarly, a second sensing channel 814 provides input to a second eventdetector 816, and a third input channel 818 and a fourth input channel820 both provide input to a third event detector 822. Additionally, inembodiments using only a single channel, either one of the eventdetectors 812, 816, described below in greater detail, can be used.However, other configurations can also be used.

Considering the processing performed by the event detectors 812, 816,and 822, the first input channel 810 feeds a signal to both a wavemorphology analysis unit 824 (one of the wave morphology analysis units712 of FIG. 7) and a window analysis unit 826 (one of the windowanalysis units 714 of FIG. 7). The window analysis unit 826, in turn,includes a line length analysis tool 828 and an area analysis tool 830.As discussed in detail below, the line length analysis tool 828 and thearea analysis tool 830 can be configured to analyze different aspects ofthe signal from the first input channel 810.

Outputs from the wave morphology analysis unit 824, the line lengthanalysis tool 828, and the area analysis tool 830 can be combined in aBoolean AND operation 832 and sent to an output 834 for further use by asystem according to an embodiment. For example, if a combination ofanalysis tools in an event detector identifies several simultaneous (ornear-simultaneous) types of activity in an input channel, a systemaccording to an embodiment can be programmed to perform an action inresponse thereto. Exemplary details of the analysis tools and thecombination processes that can be used in the event detectors aredescribed in greater detail below.

In the second event detector 816, only a wave morphology analysis unit836 is active. Accordingly, no Boolean operation needs to be performed,and the wave morphology analysis unit 836 directly feeds an eventdetector output 838.

The third event detector 822 can operate on two input channels 818 and820, and can include two separate detection channels of analysis units:a first wave morphology analysis unit 840 and a first window analysisunit 842, the latter including a first line length analysis tool 844 anda first area analysis tool 846; and a second wave morphology analysisunit 848 and a second window analysis unit 850, the latter including asecond line length analysis tool 852 and a second area analysis tool854. The two detection channels of analysis units can be combined toprovide a single event detector output 856.

In the first detection channel of analysis units 840 and 842, outputsfrom the first wave morphology analysis unit 840, the first line lengthanalysis tool 844, and the first area analysis tool 846 can be combinedvia a Boolean AND operation 858 into a first detection channel output860. Similarly, in the second detection channel of analysis units 848and 850, outputs from the second wave morphology analysis unit 848, thesecond line length analysis tool 852, and the second area analysis tool854 can be combined via a Boolean AND operation 862 into a seconddetection channel output 864. In the illustrated embodiment, the seconddetection channel output 864 is invertible with selectable Boolean logicinversion 866 before it is combined with the first detection channeloutput 860. Subsequently, the first detection channel output 860 and thesecond detection channel output 864 are combined with a Boolean ANDoperation 868 to provide a signal to the output 856. In an alternativeembodiment, a Boolean OR operation is used to combine the firstdetection channel output 860 and the second detection channel output864.

In some embodiments, the second detection channel (analysis units 848and 850) represents a “qualifying channel” with respect to the firstdetection channel (analysis units 840 and 842). In general, a qualifyingchannel allows a detection to be made only when both channels are inconcurrence with regard to detection of an event. For example, aqualifying channel can be used to indicate when a seizure has“generalized,” i.e. spread through a significant portion of a patient'sbrain. To do this, the third input channel 818 and the fourth inputchannel 820 can be configured to receive EEG waveforms from separateamplifier channels coupled to electrodes in separate parts of thepatient's brain (e.g., in opposite hemispheres). Accordingly, then, theBoolean AND operation 868 will indicate a detection only when the firstdetection output 860 and the second detection output 864 both indicatethe presence of an event (or, when Boolean logic inversion 866 ispresent, when the first detection output 860 indicates the presence ofan event while the second detection output 864 does not). As describedin further detail below, the detection outputs 860 and 864 can beprovided with selectable persistence (i.e., the ability to remaintriggered for some time after the event is detected), allowing theBoolean AND combination 868 to be satisfied even when there is notprecise temporal synchronization between detections on the two channels.

It should be appreciated that the concept of a “qualifying channel”allows the flexible configuration of a device 110 to achieve a number ofadvantageous results. In addition to the detection of generalization, asdescribed above, a qualifying channel can be configured, for example, todetect noise so a detection output is valid only when noise is notpresent, to assist in device configuration in determining which of twosets of detection parameters is preferable (by setting up the differentparameters in the first detection channel and the second detectionchannel, then replacing the Boolean AND combination with a Boolean ORcombination), or to require a specific temporal sequence of detections(which would be achieved in software by the CPU 428 after a Boolean ORcombination of detections). There are numerous other possibilities.

The outputs 834, 838, and 856 of the event detectors are preferablyrepresented by Boolean flags, and as described below, provideinformation for the operation of a system according to an embodiment.

While FIG. 8 illustrates four different sensing channels providing inputto four separate detection channels, it should be noted that maximallyflexible embodiments would allow each sensing channel to be connected toone or more detection channels. It can be advantageous to program thedifferent detection channels with different settings (e.g., thresholds)to facilitate alternate “views” of the same sensing channel data stream.

FIG. 9 illustrates three representative waveforms of the type expectedto be manipulated by a system according to some embodiments. It shouldbe noted, however, that the waveforms illustrated in FIG. 9 areillustrative only, and are not intended to represent any actual data.The first waveform 910 is representative of an unprocessedelectroencephalogram (EEG) or electrocorticogram (ECOG) waveform havinga substantial amount of variability; the illustrated segment has aduration of approximately 160 ms and a dominant frequency (visible asthe large-scale crests and valleys) of approximately 12.5 Hz. It will berecognized that the first waveform is rather rough and peaky; there is asubstantial amount of high-frequency energy represented therein.

The second waveform 912 represents a filtered version of the originalEEG waveform 910. As shown, most of the high-frequency energy has beeneliminated from the signal, and the waveform 912 is significantlysmoother. In the disclosed embodiment, this filtering operation isperformed in the sensing front end 512 before the analog to digitalconverters 612 (FIG. 6).

The filtered waveform 912 can then be sampled by one of the analog todigital converters 612; this operation is represented graphically in thethird waveform 914 of FIG. 9. As illustrated, a sample rate used in someembodiments is 250 Hz (4 ms sample duration), resulting in approximately40 samples over the illustrated 160 ms segment. As is well known in theart of digital signal processing, the amplitude resolution of eachsample is limited; in some embodiments, each sample is measured with aresolution of 10 bits (or 1024 possible values). As is apparent uponvisual analysis of the third waveform, the dominant frequency componenthas a wavelength of approximately 20 samples, which corresponds to thedominant frequency of 12.5 Hz.

Referring now to FIG. 10, the processing of the wave morphology analysisunits 712 is described in conjunction with a filtered and sampledwaveform 1010 of the type illustrated as the third waveform 914 of FIG.9.

In a first half wave 1012, which is partially illustrated in FIG. 10(the starting point occurs before the illustrated waveform segment 1010begins), the waveform segment 1010 is essentially monotonicallydecreasing, except for a small first perturbation 1014. Accordingly, thefirst half wave 1012 is represented by a vector from the starting point(not shown) to a first local extremum 1016, where the waveform starts tomove in the opposite direction. The first perturbation 1014 is ofinsufficient amplitude to be considered a local extremum, and isdisregarded by a hysteresis mechanism (discussed in further detailbelow).

A second half wave 1018 extends between the first local extremum 1016and a second local extremum 1020. Again, a second perturbation 1022 isof insufficient amplitude to be considered an extremum. Likewise, athird half wave 1024 extends between the second local extremum 1020 anda third local extremum 1026; this may appear to be a small perturbation,but is greater in amplitude than a selected hysteresis threshold. Theremaining half waves 1028, 1030, 1032, 1034, and 1036 are identifiedanalogously. As will be discussed in further detail below, each of theidentified half waves 1012, 1018, 1024, 1028, 1030, 1032, 1034, and 1036has a corresponding duration 1038, 1040, 1042, 1044, 1046, 1048, 1050,and 1052, respectively, and analogously, a corresponding amplitudedetermined from the relative positions of each half wave's startingpoint and ending point along the vertical axis, and a slope direction,increasing or decreasing.

In a method performed according to some embodiments, it is particularlyadvantageous to allow for a programmable hysteresis setting inidentifying the ends of half waves. In other words, as explained above,the end of an increasing or decreasing half wave might be prematurelyidentified as a result of quantization (and other) noise, low-amplitudesignal components, and other perturbing factors, unless a smallhysteresis allowance is made before a reversal of waveform direction(and a corresponding half wave end) is identified. Hysteresis allows forinsignificant variations in signal level inconsistent with the signal'soverall movement to be ignored without the need for extensive furthersignal processing such as filtering. Without hysteresis, such small andinsignificant variations might lead to substantial and gross changes inwhere half waves are identified, leading to unpredictable results.

The processing steps performed with regard to the waveform 1010 and halfwaves of FIG. 10 are set forth in FIG. 11. The method begins byidentifying an increasing half wave (with an ending amplitude higherthan the starting amplitude, as in the second half wave 1018 of FIG.10). To do this, a variable corresponding to half wave time is firstinitialized to zero (step 1110); then half wave duration, endingthreshold, peak amplitude, and first sample value are all initialized(step 1112). Specifically, the half wave duration value is set to zero;the peak amplitude and first sample values are set to the amplitudevalue of the last observed sample, which as described above is a valuehaving 10-bit precision; and the ending threshold is set to the lastobserved sample minus a small preset hysteresis value. After waiting fora measurement of the current EEG sample (step 1114), the half wave timeand half wave duration variables are incremented (step 1116). If thecurrent EEG sample has an amplitude greater than the peak amplitude(step 1118), then the amplitude of the half wave is increasing (orcontinues to increase). Accordingly, the ending threshold is reset to bethe current EEG sample's amplitude minus the hysteresis value, and thepeak is reset to the current EEG sample's amplitude (step 1120), and thenext sample is awaited (step 1114).

If the current EEG sample has an amplitude less than the endingthreshold (step 1122), then the hysteresis value has been exceeded, anda local extremum has been identified. Accordingly, the end of theincreasing half wave has been reached, and the amplitude and duration ofthe half wave are calculated (step 1124). The amplitude is equal to thepeak amplitude minus the first sample value; the duration is equal tothe current half wave duration. Otherwise, the next ample is awaited(step 1114).

If both the amplitude and the duration qualify by exceedingcorresponding preset thresholds (step 1126), then the amplitude,duration, half wave time, half wave direction (increasing) are stored ina buffer (step 1128), and the half wave time is reset to zero (step1130).

At the conclusion of the increasing half wave, the process continues byinitializing wave duration, the ending threshold, the peak amplitude,and the first sample value (step 1132). Wave duration is set to zero,the ending threshold is set to the last sample value plus the hysteresisvalue, the peak amplitude and the first sample value are set to the mostrecent sample value.

After waiting for a measurement of the current EEG sample (step 1134),the half wave time and half wave duration variables are incremented(step 1136). If the current EEG sample has an amplitude lower than thepeak amplitude (step 1138), then the amplitude of the half wave isdecreasing (or continues to decrease). Accordingly, the ending thresholdis reset to be the current EEG sample's amplitude plus the hysteresisvalue, the peak is reset to the current EEG sample's amplitude (step1140), and the next sample is awaited (step 1134).

If the current EEG sample has an amplitude greater than the endingthreshold (step 1142), then the hysteresis value has been exceeded, anda local extremum has been identified. Accordingly, the end of thedecreasing half wave has been reached, and the amplitude and duration ofthe half wave are calculated (step 1144). The amplitude is equal to thefirst sample value minus the peak amplitude, and the duration is equalto the current half wave duration. Otherwise, the next EEG sample isawaited (step 1134).

If both the amplitude and the duration qualify by exceedingcorresponding preset thresholds (step 1146), then the amplitude,duration, half wave time, half wave direction (decreasing) are stored ina buffer (step 1148), and the half wave time is reset to zero (step1150). It should be noted that, in the context of this specification,the term “exceed” in regard to a threshold value means to meet aspecified criterion. Generally, to exceed a threshold herein is to havea numeric value greater than or equal to the threshold, although otherinterpretations (such as greater than, or less than, or less than orequal to, depending on the context) may be applicable and are deemed tobe within the scope of the present inventions.

At the conclusion of the decreasing half wave, further half waves arethen identified by repeating the process from step 1112. As half wavedetection is an ongoing and continuous process, this procedurepreferably does not exit, but may be suspended from time to time whenconditions or device state call for it, e.g. when the device isinactive. Once suspended, the procedure should recommence with the firstinitialization step 1110.

Accordingly, the process depicted in FIG. 11 stores parameterscorresponding to qualified half waves, including their directions,durations, amplitudes, and the elapsed time between adjacent qualifiedhalf waves (i.e. the half wave time variable). In some embodiments, toreduce power consumption, this procedure is performed in customelectronic hardware; it should be clear that the operations of FIG. 11can be performed in parallel for each active instance of the wavemorphology analysis units 712 (FIG. 7). It should also be noted,however, that certain software can also be used to advantageous effectin this context.

This stored information is used in the software process illustrated inFIG. 12, which is performed on a periodic basis, preferably once everyprocessing window (a recurring time interval that is either fixed orprogrammable) by a system according to some embodiments. Consistent withthe other analysis tools described herein, the duration of an exemplaryprocessing window is in one embodiment, 128 ms, which corresponds to 32samples at a 250 Hz sampling rate.

Each time the software process of FIG. 12 is invoked, the half wavewindow flag is first cleared (step 1210). Any qualified half wavesidentified by the process set forth in FIG. 11 that are newly identifiedsince the last invocation of the procedure (i.e., all qualified halfwaves that ended within the preceding processing window) are identified(step 1212). A “current half wave” pointer is set to point to the oldestqualified half wave identified in the most recent processing window(step 1214). The time interval between the current half wave and theprior x half waves is then measured (step 1216), where x is a specifiedminimum number of half waves (preferably a programmable value) to beidentified within a selected half wave time window (the duration ofwhich is another programmable value) to result in the possible detectionof a neurological event. If the time interval is less than the durationof the half wave time window (step 1218), then the half wave window flagis set (step 1220), logic inversion is selectively applied (step 1222),and the procedure ends (step 1224). Logic inversion, a mechanism fordetermining whether an analysis unit is triggered by the presence orabsence of a condition, is explained in greater detail below. Otherwise,the current half wave pointer is incremented to point to the next newhalf wave (step 1228), and if there are no more new half waves (step1230), logic inversion is applied if desired (step 1222), and theprocedure ends (step 1224). Otherwise, the next time interval is tested(step 1216) and the process continues from there.

Logic inversion allows the output flag for the wave morphology analysisunit (or any other analyzer) to be selectively inverted. If logicinversion is configured to be applied to an output of a particularanalysis unit, then the corresponding flag will be clear when thedetection criterion (e.g., number of qualified half waves) is met, andset when the detection criterion is not met. This capability providessome additional flexibility in configuration, facilitating detection ofthe absence of certain signal characteristics when, for example, thepresence of those characteristics is the norm.

In some embodiments, the half wave window flag (set in step 1220)indicates whether a sufficient number of qualified half waves occur overan interval ending in the most recent processing window. To reduce theoccurrence of spurious detections, an X of Y criterion is applied,causing the wave morphology analysis unit to trigger only if asufficient number of qualified half waves occur in X of the Y mostrecent processing windows, where X and Y are parameters individuallyadjustable for each analysis tool. This process is illustrated in FIG.13.

Initially, a sum (representing recent processing windows having the halfwave window flag set) is cleared to zero and a current window pointer isinitialized to point to the most recent processing window (step 1310).If the half wave window flag corresponding to the current window pointeris set (step 1312), then the sum is incremented (step 1314). If thereare more processing windows to examine (for an X of Y criterion, a totalof Y processing windows, including the most recent, should beconsidered) (step 1316), then the window pointer is decremented (step1318) and the flag testing and sum incrementing steps (steps 1312-1314)are repeated.

After Y windows have been considered, if the sum of windows having sethalf wave window flags meets the threshold X (step 1320), then the halfwave analysis flag is set (step 1322), persistence (described below) isapplied (step 1324), and the procedure is complete. Otherwise, the halfwave analysis flag is cleared (step 1326).

Persistence, another per-analysis-tool setting, allows the effect of anevent detection (a flag set) to persist beyond the end of the detectionwindow in which the event occurs. In some embodiments, persistence canbe set anywhere from one second to fifteen seconds (though othersettings are possible), so if detections with multiple analysis tools donot all occur simultaneously (though they should still occur within afairly short time period), a Boolean combination of flags will stillyield positive results. Persistence can also be used with a singleanalysis tool to smooth the results.

When the process of FIG. 13 is completed, the half wave analysis flag(set or cleared in steps 1322 and 1326, respectively) indicates whetheran event has been detected in the corresponding channel of the wavemorphology analysis units 712, or stated another way, whether asufficient number of qualified half waves have appeared in X of the Ymost recent processing windows. Although in the disclosed embodiment,the steps of FIGS. 12 and 13 are performed in software, it should berecognized that some or all of those steps can be performed using customelectronics, if it proves advantageous in the desired application to usesuch a configuration.

FIG. 14 illustrates the waveform of FIG. 9, further depicting linelengths identified within a time window. The time window used withrespect to FIGS. 14-16 may be different from the half wave processingwindow described above in connection with FIGS. 12-13, but in someembodiments, refers to the same time intervals. From an implementationstandpoint, a single device interrupt upon the conclusion of eachprocessing window allows all of the analysis tools to perform thenecessary corresponding software processes; the line length analysisprocess of FIG. 16 (described below) is one such example. A waveform1410 is a filtered and otherwise pre-processed EEG signal as received inone of the window analysis units 714 from the sensing front end 512. Asdiscussed above, line lengths are considered within time windows. Asillustrated in FIG. 14, the duration of an exemplary window 1412 is 32samples, which is equivalent to 128 ms at a 250 Hz sampling rate.

The total line length for the window 1412 is the sum of thesample-to-sample amplitude differences within that window 1412. Forexample, the first contribution to the line length within the window1412 is a first amplitude difference 1414 between a previous sample 1416occurring immediately before the window 1412 and a first sample 1418occurring within the window 1412. The next contribution comes from asecond amplitude difference 1420 between the first sample 1418 and asecond sample 1422; a further contribution 1424 comes from a thirdamplitude difference between the second sample 1422 and a third sample1426; and so on. At the end of the window 1412, the final contributionto the line length comes from a last amplitude difference 1430 between asecond-last sample 1432 in the window 1412 and a last sample 1434 in thewindow 1412. Note that all line lengths, whether increasing ordecreasing in direction, are accumulated as positive values;accordingly, a decreasing amplitude difference 1414 and an increasingamplitude difference 1428 both contribute to a greater line length.

As illustrated herein, and as discussed in detail above, there arethirty-two samples within the window 1412. The illustrated window 1412has a duration of 128 ms, and accordingly, the illustrated sample rateis 250 Hz. It should be noted, however, that alternate window durationsand sample rates are possible and considered to be appropriate for someembodiments.

The line lengths illustrated in FIG. 14 can be calculated as shown bythe flow chart of FIG. 15, which is invoked at the beginning of a timewindow. Initially, a line length total variable is initialized to zero(step 1510). The current sample is awaited (step 1512), and the absolutevalue of the amplitude difference between the current sample and theprevious sample (which, when considering the first sample in a window,may come from the last sample in a previous window) is measured (step1514).

In various alternative embodiments, either the measured difference (ascalculated in step 1514, described above), or the sample values used tocalculate the difference can be mathematically transformed in usefulnonlinear ways. For example, it may be advantageous in certaincircumstances to calculate the difference between adjacent samples usingthe squares of the sample values, or to calculate the square of thedifference between sample values, or both. It is contemplated that othertransformations (such as square root, exponentiation, logarithm, andother nonlinear functions) might also be advantageous in certaincircumstances. Whether or not to perform such a transformation and thenature of any transformation to be performed are preferably programmableparameters of the device 110.

For use in the next iteration, the previous sample is replaced with thevalue of the current sample (step 1516), and the calculated absolutevalue is added to the total (step 1518). If there are more samplesremaining in the window 1412 (step 1520), another current sample isawaited (step 1512) and the process continues. Otherwise, the linelength calculation for the window 1412 is complete, and the total isstored (step 1522), the total is re-initialized to zero (step 1510), andthe process continues.

As with the half wave analysis method set forth above, the line lengthcalculation does not need to terminate; it can be free-running yetinterruptible. If the line length calculation is restarted after havingbeen suspended, it should be re-initialized and restarted at thebeginning of a window. This synchronization can be accomplished throughhardware interrupts.

The line lengths calculated as shown in FIG. 15 are then processed asindicated in the flow chart of FIG. 16, which is performed after eachwindow 1412 is calculated and stored (step 1522).

The process begins by calculating a running accumulated line lengthtotal over a period of n time windows. Where n>1, the effect is that ofa sliding window; in an alternative embodiment an actual sliding windowprocessing methodology may be used.

First, the accumulated total is initialized to zero (step 1610). Acurrent window pointer is set to indicate the n^(th)-last window, i.e.,the window (n−1) windows before the most recent window (step 1612). Theline length of the current window is added to the total (step 1614), thecurrent window pointer is incremented (step 1616), and if there are morewindows between the current window pointer and the most recent (last)window (step 1618), the adding and incrementing steps (1614-1616) arerepeated. Accordingly, by this process, the resulting total includes theline lengths for each of the n most recent windows.

In some embodiments, the accumulated total line length is compared to adynamic threshold, which is based on a trend of recently observed linelengths. The trend is recalculated regularly and periodically, aftereach recurring line length trend interval (which is preferably a fixedor programmed time interval). Each time the line length trend intervalpasses (step 1620), the line length trend is calculated or updated (step1622). In some embodiments, this is accomplished by calculating anormalized moving average of several trend samples, each of whichrepresents several consecutive windows of line lengths. A new trendsample is taken and the moving average is recalculated upon every linelength trend interval. The number of trend samples used in thenormalized moving average and the number of consecutive windows of linelength measurements per trend sample are preferably both fixed orprogrammable values.

After the line length trend has been calculated, the line lengththreshold is calculated (step 1624) based on the new line length trend.In some embodiments, the threshold can be set as either a percentage ofthe line length trend (either below 100% for a threshold that is lowerthan the trend, or above 100% for a threshold that is higher than thetrend) or alternatively a fixed numeric offset from the line lengthtrend (either negative for a threshold that is lower than the trend, orpositive for a threshold that is higher than the trend). Other methodsfor deriving a numeric threshold from a numeric trend can also be usedin accordance with some embodiments.

The first time the process of FIG. 16 is performed, there is generallyno line length trend against which to set a threshold. Accordingly, forthe first several passes through the process (until a sufficient amountof EEG data has been processed to establish a trend), the threshold isessentially undefined and the line length detector should not return apositive detection. Some “settling time” thus can be used to establishtrends and thresholds before detections are made.

If the accumulated line length total exceeds the calculated threshold(step 1626), then a flag is set (step 1628) indicating aline-length-based event detection on the current window analysis unitchannel 714. As described above, in some embodiments, the threshold isdynamically calculated from a line length trend, but alternatively, thethreshold may be static, either fixed or programmed into the device 110.If the accumulated line length total does not exceed the threshold, theflag is cleared (step 1630). Once the line length flag has been eitherset or cleared, logic inversion is applied (step 1632), persistence isapplied (step 1634), and the procedure terminates.

The resulting persistent line length flag indicates whether thethreshold has been exceeded within one or more windows over a timeperiod corresponding to the line length flag persistence. As discussedin further detail below, line length event detections can be combinedwith the half wave event detections, as well as any other applicabledetection criteria according to some embodiments.

FIG. 17 illustrates the waveform of FIG. 9 with area under the curveidentified within a window. Area under the curve, which in somecircumstances is somewhat representative of a signal's energy (thoughenergy of a waveform is more accurately represented by the area underthe square of a waveform), is another detection criterion that can beused in accordance with some embodiments.

The total area under the curve represented by a waveform 1710 within thewindow 1712 is equal to the sum of the absolute values of the areas ofeach rectangular region of unit width vertically bounded by thehorizontal axis and the sample. For example, the first contribution tothe area under the curve within the window 1712 comes from a firstregion 1714 between a first sample 1716 and a baseline 1717. A secondcontribution to the area under the curve within the window 1712 comesfrom a second region 1718, including areas between a second sample 1720and the baseline 1717. There are similar regions and contributions for athird sample 1722 and the baseline 1717, a fourth sample 1724 and thebaseline 1717, and so on. It should be observed that the region widthsare not important—the area under each sample can be considered theproduct of the sample's amplitude and a unit width, which can bedisregarded. In a similar manner, each region is accumulated and addedto the total area under the curve within the window 1712. Although theconcept of separate rectangular regions is a useful construct forvisualizing the idea of area under a curve, it should be noted that aprocess for calculating area need not partition areas into regions asshown in FIG. 17—it is only necessary to accumulate the absolute valueof the waveform's amplitude at each sample, as the unit width of eachregion can be disregarded. The process for doing this will be set forthin detail below in connection with FIG. 18.

The areas under the curve illustrated in FIG. 17 are calculated as shownby the flow chart of FIG. 18, which is invoked at the beginning of atime window. Initially, an area total variable is initialized to zero(step 1810). The current sample is awaited (step 1812), and the absolutevalue of the current sample is measured (step 1814).

As with the line length calculation method described above (withreference to FIG. 15), in various alternative embodiments, the currentsample (as measured in step 1814, described above) may be mathematicallytransformed in useful nonlinear ways. For example, it may beadvantageous in certain circumstances to calculate the square of thecurrent sample rather than its absolute value. The result of such atransformation by squaring each sample will generally be morerepresentative of signal energy, though it is contemplated that othertransformations (such as square root, exponentiation, logarithm, andother nonlinear functions) might also be advantageous in certaincircumstances. Whether or not to perform such a transformation and thenature of any transformation to be performed are preferably programmableparameters of the device 110.

The calculated absolute value is added to the total (step 1816). Ifthere are more samples remaining in the window 1712 (step 1818), anothercurrent sample is awaited (step 1812) and the process continues.Otherwise, the area calculation for the window 1712 is complete, and thetotal is stored (step 1820), the total is reinitialized to zero (step1810), and the process continues.

As with the half wave and line length analysis methods set forth above,the area calculation does not need to terminate; it can be free-runningyet interruptible. If the area calculation is restarted after havingbeen suspended, it should be re-initialized and restarted at thebeginning of a window. This synchronization can be accomplished throughhardware interrupts.

The line lengths calculated as shown in FIG. 18 are then processed asindicated in the flow chart of FIG. 19, which is performed after eachwindow 1712 is calculated and stored (step 1820).

The process begins by calculating a running accumulated area total overa period of n time windows. Where n>1, the effect is that of a slidingwindow; in an alternative embodiment an actual sliding window processingmethodology may be used. First, the accumulated total is initialized tozero (step 1910). A current window pointer is set to indicate then^(th)-last window, i.e., the window (n−1) windows before the mostrecent window (step 1912). The area for the current window is added tothe total (step 1914), the current window pointer is incremented (step1916), and if there are more windows between the current window and themost recent (last) window (step 1918), the adding and incrementing steps(1914-1916) are repeated. Accordingly, by this process, the resultingtotal includes the areas under the curve for each of the n most recentwindows.

In some embodiments, the accumulated total area can be compared to adynamic threshold, which can be based on a trend of recently observedareas. The trend can be recalculated regularly and periodically, aftereach recurring area trend interval (which is preferably a fixed orprogrammed time interval). Each time the area trend interval passes(step 1920), the area trend can be calculated or updated (step 1922). Insome embodiments, this can be accomplished by calculating a normalizedmoving average of several trend samples, each of which representsseveral consecutive windows of areas. A new trend sample can be takenand the moving average is recalculated upon every area trend interval.The number of trend samples used in the normalized moving average andthe number of consecutive windows of area measurements per trend sampleare preferably both fixed or programmable values.

After the area trend has been calculated, the area threshold can becalculated (step 1924) based on the new area trend. As with line length,discussed above, the threshold can be set as either a percentage of thearea trend (either below 100% for a threshold that is lower than thetrend, or above 100% for a threshold that is higher than the trend) oralternatively a fixed numeric offset from the area trend (eithernegative for a threshold that is lower than the trend, or positive for athreshold that is higher than the trend).

The first time the process of FIG. 19 is performed, there is generallyno area trend against which to set a threshold. Accordingly, for thefirst several passes through the process (until a sufficient amount ofEEG data has been processed to establish a trend), the threshold isessentially undefined and the area detector should not return a positivedetection. Some “settling time” thus can be used to establish trends andthresholds before a detection can be made.

If the accumulated total exceeds the calculated threshold (step 1926),then a flag is set (step 1928) indicating an area-based event detectionon the current window analysis unit channel 714. Otherwise, the flag iscleared (step 1930). Once the area flag has been either set or cleared,logic inversion is applied (step 1932), persistence is applied (step1934), and the procedure terminates.

The resulting persistent area flag indicates whether the threshold hasbeen exceeded within one or more windows over a time periodcorresponding to the area flag persistence. As discussed in furtherdetail below, area event detections can be combined with the half waveevent detections, line length event detections, as well as any otherapplicable detection criteria according to some embodiments.

In some embodiments, each threshold for each channel and each analysistool can be programmed separately; accordingly, a large number ofindividual thresholds can be used. It should be noted thresholds canvary widely; they can be updated by a physician via the externalprogrammer 312 (FIG. 3), and some analysis tool thresholds (e.g., linelength and area) can also be automatically varied depending on observedtrends in the data. This is preferably accomplished based on a movingaverage of a specified number of window observations of line length orarea, adjusted as desired via a fixed offset or percentage offset, andmay compensate to some extent for diurnal and other normal variations inbrain electrophysiological parameters.

With regard to the flow charts of FIGS. 11-13, 15-16, and 18-19, itshould be noted that there can be a variety of ways these processes areimplemented. For example, state machines, software, hardware (includingASICs, FPGAs, and other custom electronics), and various combinations ofsoftware and hardware, are all solutions that would be possible topractitioners of ordinary skill in the art of electronics and systemsdesign. It should further be noted that the steps performed in softwareneed not be, as some of them can be implemented in hardware, if desired,to further reduce computational load on the processor. In the context ofthe present embodiments, it is not believed to be advantageous to havethe software perform additional steps, as that would likely increasepower consumption.

In some embodiments, one of the detection schemes set forth above (e.g.,half wave detection) can be adapted to use an X of Y criterion to weedout spurious detections. This can be implemented via a shift register,as usual, or by more efficient computational methods. As describedabove, half waves are analyzed on a window-by-window basis, and asdescribed above (in connection with FIG. 13), the window results areupdated on a separate analysis window interval. If the detectioncriterion (i.e., a certain number of half waves in less than a specifiedtime period) is met for any of the half waves occurring in the mostrecent window, then detection is satisfied within that window. If thatoccurs for at least X of the Y most recent windows, then the half waveanalysis tool triggers a detection. If desired, other detectionalgorithms (such as line length and area) may operate in much the sameway: if thresholds are exceeded in at least X of the Y most recentwindows, then the corresponding analysis tool triggers a detection.

Also, in the disclosed embodiment, each detection flag, after being set,remains set for a selected amount of time, allowing them to be combinedby Boolean logic (as described below) without necessarily beingsimultaneous.

As indicated above, each of the software processes set forth above(FIGS. 12-13, 16, and 19) correspond to functions performed by the wavemorphology analysis units 712 and window analysis units 714. Each one isinitiated periodically, typically once per detection window (1212,1512). The outputs from the half wave and window analysis units 712 and714, namely the flags generated in response to counted qualified halfwaves, accumulated line lengths, and accumulated areas are combined toidentify event detections as functionally illustrated in FIG. 8 and asdescribed via flow chart in FIG. 20.

The process begins with the receipt of a timer interrupt (step 2010),which is typically generated on a regular periodic basis to indicate theedges of successive time windows. Accordingly, in a system or method insome embodiments, such a timer interrupt is received every 128 ms, or asotherwise programmed or designed. Then the half wave (step 2012, FIGS.12-13), line length (step 2014, FIG. 16), and area (step 2016, FIG. 19)analysis tools are evaluated with respect to the latest data generatedthereby, via the half wave analysis flag, the line length flag, and thearea flag for each active channel. The steps of checking the analysistools (steps 2012, 2014, and 2016) can be performed in any desired orderor in parallel, as they are generally not interdependent. It should benoted that the foregoing analysis tools should be checked for everyactive channel, and may be skipped for inactive detection channels.

Flags, indicating whether particular signal characteristics have beenidentified in each active channel, for each active analysis tools, canthen be combined into detection channels (step 2018) as illustrated inFIG. 8. In some embodiments, this operation is performed as described indetail below with reference to FIG. 21. Each detection channel is aBoolean AND combination of analysis tool flags for a single channel, andas disclosed above, there can be one or more channels in a systemaccording to some embodiments.

The flags for multiple detection channels are then combined into eventdetector flags (step 2020), which are indicative of identifiedneurological events calling for action by the device. This process isdescribed below, see FIG. 20, and is in general a Boolean combination ofdetection channels, if there is more than one channel per eventdetector.

If an event detector flag is set (step 2022), then a correspondingaction is initiated (step 2024) by the device. Actions according to someembodiments can include the presentation of a warning to the patient, aninitiation of a device mode change, or a recording of certain EEGsignals or other data; it will be appreciated that there are numerousother possibilities. It is preferred, but not necessary, for actionsinitiated by a device according to some embodiments to be performed inparallel with the sensing and detection operations described in detailherein.

Multiple event detector flags are possible, each one representing adifferent combination of detection channel flags. If there are furtherevent detector flags to consider (step 2026), those event detector flagscan also be evaluated (step 2022) and may cause further actions by thedevice (step 2024). It should be noted that, in general, actionsperformed by the device (as in step 2024) may be in part dependent on adevice state—even if certain combinations of events do occur, no actionmay be taken if the device is in an inactive state, for example.

As described above, and as illustrated in FIG. 20 as step 2018, acorresponding set of analysis tool flags is combined into a detectionchannel flag as shown in FIG. 21 (see also FIG. 8). Initially the outputdetection channel flag is set (step 2110). Beginning with the firstanalysis tool for a particular detection channel (step 2112), if thecorresponding analysis tool flag is not set (step 2114), then the outputdetection channel flag is cleared (step 2116).

If the corresponding analysis tool flag is set (step 2114), the outputdetection channel flag remains set, and further analysis tools for thesame channel, if any (step 2118), are evaluated. Accordingly, thiscombination procedure operates as a Boolean AND operation—if any of theenabled and active analysis tools for a particular detection channeldoes not have a set output flag, then no detection channel flag isoutput by the procedure.

A clear analysis tool flag indicates that no detection has been madewithin the flag persistence period, and for those analysis tools thatemploy an X of Y criterion, that such criterion has not been met. Incertain circumstances, it may be advantageous to also provide detectionchannel flags with logic inversion. Where a desired criterion (i.e.,combination of analysis tools) is not met, the output flag is set(rather than cleared, which is the default action). This can beaccomplished by providing selectable Boolean logic inversion (step 2120)corresponding to each event detector.

Also as described above, and as illustrated in FIG. 20 as step 2020,multiple detection channel flags are combined into a single eventdetector flag as shown in FIG. 22 (see also FIG. 8). Initially theoutput event detector flag is set (step 2210). Beginning with the firstdetection channel for a particular event detector (step 2212), if thechannel is not enabled (step 2214), then no check is made. If thechannel is enabled and the corresponding detection channel flag is notset (step 2216), then the output event detector flag is cleared (step2218) and the combination procedure exits. If the correspondingdetection channel flag is set (step 2216), the output event detectorflag remains set, and further detection channels, if any (step 2220),are evaluated after incrementing the channel being considered (step2222). Accordingly, this combination procedure also operates as aBoolean AND operation—if any of the enabled and active detectionchannels does not have a set output flag, then no event detector flag isoutput by the procedure. It should also be observed that a Boolean ORcombination of detection channels may provide useful information incertain circumstances; a software or hardware flow chart accomplishingsuch a combination is not illustrated, but could easily be created by anindividual of ordinary skill in digital electronic design or computerprogramming.

With reference again to FIG. 20, in some embodiments, the actions takenin the step 2024 can include logging the event flags, logging a summaryof the event flags, logging a single event as a result of one or moreflags being set, saving the EEG signals detected by the channel, savingportions of EEG signals detected before, during, or after any of theabove-noted flags are set, and recording or saving any data generatedduring the analysis of waveforms noted above, and the like.

In some embodiments, such data can be saved in the memory 516, or anyother memory device included in the device 110, in the form of tabulateddata, or in any other form. An exemplary data table 2300 is illustratedin FIG. 23. In some embodiments, the tabulated data can include a datestamp indicating the date upon which a neurological event, such as thoseassociated with the above-noted detection flags, is detected. Further,the data can include a time stamp indicating the time at which one ofthe above-noted flags is set.

FIG. 23A illustrates an exemplary but nonlimiting example of a reportthat can be generated with data collected by the implantable seizuremonitor 110. At the top of the exemplary report of FIG. 23A are a numberof data fields that can include, for example, but without limitation,the patient's name, the date of the report, the name of the physician,the date range during which recordings were taken, and a key identifyingwhen medications have changed or when other clinically significantevents occur.

Below these data fields is a chart indicating the number of seizuresidentified by the implantable recording device 110. This chart islabeled “seizure counts”. The vertical axis of this graph indicates thenumber of seizures counted. The horizontal axis can serve as a timeline.In this case, the days on which seizures were counted are identified asDay W, Day X, Day Y, and Day Z. On the days identified as W, X, and Z,one seizure was counted on each day. On the day labeled Day Y, twoseizures were counted.

Below the seizure count chart are samples of brainwave recordings thatcan be captured by the implantable recording device 110. Each of theserecordings are identified corresponding to the day and time at whichthey were recorded.

As noted above, the seizure report of FIG. 23A is merely an exemplaryreport that can be generated from the data captured by the implantablerecording device 110. Other reports can also be generated. Further, suchreports can be organized in different ways and can include other ordifferent information.

Such tabulated data can include an indication of the type of flag thathas been set, such as, for example, but without limitation, the areaflag of step 1928, the set line length flag of step 1628, and thehalfway flag of step 1322, and/or other flags.

As shown in FIG. 23, the tabulated data can optionally include anindication of the severity of the neurological event. For example, theroutine illustrated in FIG. 19 can be modified to include an additionaloperation to save to memory the area trend calculated in step 1922. Thissaved area calculation can then be stored in the table 2300 if the areaflag is set in step 1928. Alternatively, other calculations can also beused to create an indication of the severity of the event causing thearea flag to be set in step 1928.

Similarly, the routine of FIG. 16 can be modified to include anadditional operation of saving the value of the calculation of the linelength trend in step 1622. In such embodiments, this routine can also bemodified to save the line length trend value calculated from step 1622of FIG. 16 to the table 2300 when the line length flag is set in steps1628. However, other calculations can also be used to create anindication of the severity of the neurological event causing the linelength flag to be set in step 1628.

Additionally, the routine of FIG. 13 can be further modified to save thevalue generated from the sum of step 1314 when the half-wave flag is setin step 1322. Additionally, the routine can be modified to save thevalue of the sum from step 1314 to the table 2300 so as to provide anindication of the severity of the neurological event causing thehalf-wave flag to be set in step 1322. However, other calculations canalso be made to provide an indication of the severity of the eventcausing the half-wave flag to be set in step 1322.

In some embodiments, the routine of FIG. 20 can be modified to save dataassociated with half-wave, line length, or area analyses performed insteps 2012, 2014, 2016, or other analyses. For example, the routine ofFIG. 20 can be modified to include an additional operation associatedwith the operation block 2024 in which an event is logged on the table2300 when any flags are indicated as being set in any of the operationblocks 2012, 2014, 2016. Such a tabulated dataset can include a datestamp and/or time stamp. Further, such a dataset can also include anindication of the severity of the neurological event triggering any ofthe flags associated with the operation blocks 2012, 2014, 2016, asillustrated in FIG. 23.

In some embodiments, the routine of FIG. 20 can be configured to log aneurological event only if all of a half-wave flag, a line length flag,and an area flag are set as a result of operation blocks 2012, 2014,2016. This can provide the benefit of saving memory in the memory device516 by reducing the number of events that are logged. However, otherrestrictions can also be used. For example, the routine of FIG. 20 canbe modified to log an event only if at least two flags are determined asbeing set through the operation blocks 2012, 2014, 2016. However, otheranalyses can also be used to determine when to log a neurological event.

FIGS. 24-26 illustrate a modification of the device 110 of FIG. 2 whichis identified generally by the reference numeral 110′. The recordingdevice 110′ includes some components that can be constructed inaccordance with the description noted above with respect to the device110. Other components of the device 110′ also correspond to componentsof the device 110 but include modifications. As such, those componentsare identified with the same reference numeral used in FIGS. 2 and 2 aexcept that a “′” has been added.

As shown in FIG. 24, the recording device 110′ includes the housing 126and two sensing electrodes 412, 414 connected to the electronics withinthe housing through lead wires 2402, 2404. The sensing electrodes 412,414 are spaced apart by distance identified by the numeral 2406. Thedistance 2406 can be any amount. However, in an exemplary butnonlimiting embodiment, the distance 2406 can be about 3 centimeters.This spacing provides a balance between signal strength and overall sizeof the device 110′.

The electrode spacing 2406 should be adequate to sense partial seizuresthat are only occurring in a limited region of the brain. An examplewould be a unilateral temporal lobe seizure that does not spread beyondthe temporal lobe of onset. Thus, in some embodiments, a spacing 2406 ofat least about 3 centimeters as illustrated in FIG. 24 provides asufficiently strong signal to be analyzed for purposes of detectingdesired neurological events. Further, as such, the overall length of thedevice 110′ is not substantially greater than about 3 centimeters.However, as noted above, spacings 2406 of other magnitudes can also beused.

The device 110′ also includes a cushion member 228′ in which the housing226 and the sensing electrodes 412, 414, are suspended. In someembodiments, the cushioning member 228′ can be a soft silicone rubbermaterial. However, any type of soft biocompatible material can be usedas the cushioning member 228′.

As shown in FIGS. 24 and 25, the cushioning member 228′ includes anupper outer surface 2408, a lower outer surface 2410, longitudinal endportions 2412, 2414 and front and rear portions 2416, 2418.

Preferably, the housing 226 is suspended within the cushioning member228′ such that no portion of the outer surface of the housing 226 isexposed through the outer surfaces 2408, 2410, 2412, 2414, 2416, 2418 ofthe cushioning member 228′. This helps provide the patient with enhancedcomfort by preventing the hard, and in some embodiments Titanium, outerportions of the housing 226 from contacting the inner layers of thepatient's scalp and/or cranium 214 (FIGS. 2 and 2 a).

On the other hand, preferably, at least the lower surfaces of thesensing electrodes 412, 414 are exposed through the bottom surface 2410of the cushioning member 228′. As such, an exposed outer surface of thesensing electrodes 412, 414, can come into direct contact with the innerlayers of the patient's scalp, the patient's cranium 214, and/or thepatient's dura, which enhances the ability of the sensing electrodes412, 414 to receive EEG signals.

In the illustrated arrangement of the recording device 110′, the sensingelectrodes 412, 414 are substantially held in place by the cushioningmember 228′. The illustrated configuration of the cushioning member 228′is generally the shape of a pair of wings extending from the housing226, each of the wings supporting one of the sensing electrodes 412,414.

Additionally, as shown in FIGS. 24-26, the longitudinal ends 2412, 2414,and the front and rear portions 2416, 2418 of the cushioning member 228′include rounded corners and tapered areas to provide a smooth transitionso as to minimize the effect the recording device 110′ might have incausing a portion of the patient's scalp protrude.

In an exemplary but nonlimiting embodiment, with the illustratedconfiguration, the longitudinal length 2420 of the housing 226 can beabout 1.5 centimeters. The overall width 2422 of the recording device110′ can be about 1.5 centimeters. Additionally, the sensing electrodes412, 414, can have a diameter of about 0.4 centimeters. Finally, theoverall thickness 2424 (FIG. 26) of the recording device 110′ can beabout 0.38 centimeters. However, it is to be noted that the above-noteddimensions are merely exemplary, but not limiting, and are intendedmerely to convey one possible configuration for the recording device110′. Other configurations, shapes, dimensions, and contours can also beused.

FIGS. 27-29 illustrate yet another modification of the implantableseizure recording device 110′, which is identified generally by thereference numeral 110″. The recording device 110′ includes somecomponents that can be constructed in accordance with the descriptionnoted above with respect to the devices 110, 110′. Other components ofthe device 110″ also correspond to components of the device 110′ but caninclude modifications. As such, those components are identified with thesame reference numerals used in FIG. 2, 2 a, or 24-26 except that a ‘″’has been added thereto.

As shown in FIGS. 27 and 28, the recording device 110″ can include atleast one mounting tab 2430. In some embodiments, one or more mountingtabs 2430 are mounted at each corner of the housing 226″.

The mounting tabs 2430 can have any configuration. In some embodiments,the mounting tabs 2430 project from an upper surface of the housing 226″and include an aperture on a portion of the mounting tabs 2430 thatextends outwardly from and outer edge of the housing 226″. The mountingtabs 2430 thus can be configured to provide additional anchoring pointsfor securing the recording device 110″ to the skull 214 of a patient.

In some embodiments, the mounting tabs 2430 can extend outwardly fromthe cushioning number 228″. In some embodiments, the mounting tabs 2430can be completely encased in the cushioning member 228″. In someembodiments, the top, front, and rear faces of the housing 226 can beleft exposed without any of the cushioning member to 228″ covering thosefaces. In such embodiments, it can be more desirable to leave the bottomface of the housing to 226″ covered with the cushioning member 228″.However, this is optional.

FIGS. 30 and 31, illustrate an exemplary but nonlimiting mountingposition for the recording device 110″. In this installation, thepatient has been given a craniotomy which forms an installation site2440.

The recording device 110″ is placed within the installation site 2440such that the mounting tabs 2430 extend over an outer surface of thecranium 214. In this position, screws can be inserted through theapertures defined in the mounting tabs 2430 and into the cranium 214. Assuch, the recording device 110″ can be better secured in place andmaintained within the installation site 2440.

Additionally, because of the inclusion of the cushioning member 228″,the recording device 110″ better conforms to the arching configurationof the installation site 2440, and more are particularly, the outersurface of the cortex. Thus, the recording device 110″ can be morecomfortable for the patient.

After a recording device, such as the recording devices 110, 110′, 110″,have been implanted in a patient, the amplifiers 610 within suchrecording devices can be adjusted. For example, FIG. 32 illustrates thefiltered voltage 912 described above with reference to FIG. 9. Thefiltered voltage 912 is an example of a filtered voltage that can berecorded or analyzed for purposes of diagnosis of epileptic seizures, orother disorders.

However, when devices such as on of the recording devices 110, 110′,110″ is first installed, the maximum amplitudes of voltages detected bythe sensors 412, 414 cannot be predicted. Thus, after the initialinstallation of such a device, amplifier adjustments can be made so thatthe voltages output from the amplifier 610 are within a normal operationrange for the amplifier 610, and such that the voltage output from theamplifier 610 does not reach the maximum output voltage of the amplifier610 an excessive number of times.

For example, FIG. 32 illustrates voltage traces of several otherexemplary outputs from the amplifier 610. The voltage traces 3200, 3202,and 3204 are examples of the output of the amplifier 610 at differentgain settings. The amplifier 610 can be any type of amplifier.Preferably, however, the amplifier 610 has an adjustable gain. In someembodiments, the adjustable gain feature is provided through the use ofa variable resistor. However, any type of adjustable gain amplifier canbe used.

In FIG. 32, the voltage trace 3204 is an example of the filtered outputof the amplifier 610 having been adjusted to its maximum gain. Asreflected in the voltage trace 3204, the amplifier 610 reaches itssaturation point and thus, the voltage trace 3204 reaches and remainsconstant at maximum voltage portions 3206 and 3208. Assuming that thebrainwaves generating this voltage signal are normal, i.e., notindicative of epileptic seizures, it is undesirable for the amplifier610 to reach its saturation point frequently. For example, in someembodiments, it is acceptable if the voltage output from the amplifier610 reaches its saturation point no more than about once per second.

However, if the amplifier 610 reaches its saturation point and thusoutputs maximum or minimum voltages more than about once per secondduring normal brainwave activity, then the gain of the amplifier 610 maybe too high. Thus, the gain of the amplifier 610 can be reduced untilthe signal output from the amplifier 610 reaches its maximum or minimumvalues no more than about once per second. After the gain of theamplifier 610 has been adjusted as such, the recording devices 110,110′, 110″, can be used for the diagnostic uses noted above.

One way for performing such a calibration procedure is to install arecording device, such as any of the recording devices 110, 110′, 110″,with the amplifier 610 adjusted to its maximum gain value. This isbecause it is difficult to predict, as noted above, how strong the rawdetected brainwave signals will be.

With the amplifier 610 adjusted to its maximum gain value, the patientcan be released for an amount of time that will allow the patient tohave several or more seizures. After such a time has expired, thepatient can return so that a practitioner can read the informationstored in the memory device 516 of the recording devices 110, 110′,110″, to determine if the gain of the amplifier 610 was sufficientlyhigh.

Reviewing the brainwave signals recorded by the recording devices 110,110′, 110″, will reveal to a practitioner whether or not the gain of theamplifier 610 was sufficiently high. For example, practitioners candistinguish between brainwaves indicating normal brainwave activity andbrainwaves indicating epileptic seizures. Thus, if the data recorded bythe recording device 110 includes numerous and high frequencyoccurrences of the amplifier 610 reaching its saturation point duringnormal brainwave activity, the gain of the amplifier 610 was too high.As such, the practitioner can, using for example the control interface518 (FIG. 5), reduce the gain of the amplifier 610. If, on the otherhand, the practitioner determines that the gain of the amplifier 610 wasnot high enough, for example, if the amplifier 610 never reaches itssaturation point, and us the voltage output from the amplifier 610 neverreaches a maximum voltage level, then the gain of the amplifier 610 maybe too low. Thus, the practitioner, using the control interface 518, canraise the gain of the amplifier 610.

When a seizure occurs, the EEG waveform typically appears as areciprocating waveform that reaches saturation voltage for an extendedperiod of time, for example, as shown in FIG. 34. The time at which theEEG reaches saturation voltage will depend on the gain setting withhigher gain settings producing earlier but more frequent saturationevents.

Interictal (non-seizure) baseline EEG can also have brief periods ofsaturation that may be abnormal as shown in FIG. 35 (marked by *). Thepractitioner may not want to have these brief saturation events reportedbecause they do not represent seizures or other significant neurologicalevents and they can occur quite frequently. Therefore, the device 110should be able to distinguish between sustained periods of saturationthat may be neurological events of clinical significance and briefperiods of saturation that may be frequent and not of clinicalsignificance.

For example, with reference to FIG. 33, a modification of the waveformanalyzer 514 (FIG. 5) is illustrated therein and identified generally bythe reference to numeral 514′. The waveform analyzer 514′ includes somecomponents that can be constructed in accordance with the descriptionnoted above with respect to the analyzer 514. Other components of theanalyzer 514′ also correspond to components of the analyzer 514 butinclude modifications. As such, those components are identified with thesame reference 8 used in the description of the analyzer 514 except thata “′” has been added thereto.

As shown in FIG. 33, the waveform analyzer 514′ can include an eventdetector 3300, a recording controller 3302 and a memory device 516.However, the waveform analyzer 514′ can also include other devices.

In some embodiments, the event detector 3300 can be programmed todetermine whether saturation is occurring at a predetermined rate and/orfor a predetermined sustained period of time. An exemplary waveform ofsignificant neurological event is shown in FIG. 36. The waveform data inFIG. 36 has been subdivided into a series of windows (labeled 1-14 inFIG. 36), which can be of any programmable length, but, in someembodiments can be 25-1000 msec. These windows can overlap, but areshown as non-overlapping in FIG. 36.

The waveform analyzer 514 can be configured to determine if any datapoint within any window is saturated. In the representative waveform inFIG. 36, an asterisk below the respective windows indicate which windowshave a saturated data point.

In some embodiments, the practitioner can program the event detector3300 with regard to how many windows within a continuous subset wouldneed to have a saturated data point in order for the event detector todetermine that a seizure has occurred. For example, the practitionercould specify that X out of Y contiguous windows would be required tohave a saturated event (where Y is always greater or equal to X) for theevent detector 3300 to determine a seizure or significant neurologicalevent has occurred.

The table in FIG. 36 shows various outcomes for different saturationcount criteria X and Y values for the representative waveform (a “+” ina table block indicates that detection has occurred). These types ofdetections are referred to as X/Y saturation detections.

In some embodiments, the event detector 3300 can be configured todetermine whether a seizure or other neurological event has occurredbased upon analyses of X/Y saturation detections. In such embodiments,the event detector 3300 can be programmed to determine how frequentlyX/Y saturation detections are occurring and then only reportneurological events to the practitioner if the X/Y saturation rateexceeds a certain rate of occurrence. For example, the detector 3300 canbe configured to monitor the number of times that the X/Y saturationcriteria were met in a programmable time window, but then only report anevent to the practitioner if a minimum number of programmable X/Ysaturation events were detected in a programmable time period.

For example, the detector 3300 can be programmed with a time window of 5minutes and an X/Y saturation count criterion of 5. The detector wouldthen only report a neurological event to the practitioner if 5 or moreX/Y saturation events were to have occurred in the past 5 minutes.

The recording controller 3302 can be configured to record and storerecordings of the patient's brainwaves under certain circumstances. Insome embodiments, the recording controller 3302 can be configured toutilize the memory device 516 to serve as a linear cache and a filestorage unit.

For example, in various other areas in the signal processing arts, alinear cache is a known device for storing a single stream of digitalinformation in a proper sequence. As such, the linear cache maintainsthis stream as a list of the digital blocks that make up to stream. Insome embodiments, the digital blocks can each have a unique size andunique attributes, or the blocks can have a predetermined size, forexample corresponding to a predetermined period of time such as 1second, 5 five seconds, 10 seconds, 30 seconds, etc. However, the blockscan have any size. Each block within the stream can be marked with a“presentation timestamp” which indicates when that block should bepresented to a decoding process.

The presentation timestamp can be a monotonically increasing in valueinitialized at zero when the linear cache first begins operation on astream of data. In some embodiments, the presentation timestampgenerates its own time stamp signature without any relation to any otherunderlying clocking or streaming technique. The technique used forgenerating the presentation timestamp is also utilized by any decodingprocess used to read the digital blocks in the order recorded.

In some embodiments, the recording controller 3302 time stamps eachencoded digital block of data from the sensing front end device 512 asit arrives at the recording controller 3302. In other words, therecording controller 3302 marks that block of data with the currentpresentation timestamp for the stream of data being recorded.

The recording controller 3302 can be configured to maintain a window ofblocks in a window cache, for example. The recording controller 3302 canform a window cache of digital blocks, in the order according to thepresentation timestamp values. As such, the window can contain thenewest block that arrived in the window cache of digital blocks and theoldest block that this window cache is configured to hold. The windowcache can be configured to hold any number of digital blocks.

For example, the window cache can be configured to hold the number ofdigital blocks corresponding to the amount of time to equal to 10seconds, 30 seconds, 1 minute, 10 minutes, 30 minutes, 60 minutes, orany amount of time. In some embodiments, the recording controller 3302can have an adjustable window size allowing a practitioner to adjust theduration of the window size which the window cache will hold. Thisallows the practitioner to adjust the length of the recorded brainwavesbefore a seizure is detected and after a seizure is detected.

In other words, the window cache of digital blocks represents a timespan into the past history of the stream of brainwaves coming from thesensing front-end 512. The recording controller 3302 can be configuredto discard digital blocks that fall outside the window cache. In otherwords, the recording controller 3302 can be configured to erase digitalblocks that fall outside of the window cache. As such, the window issized such that one can only look back a limited distance into the pasthistory of the datastream output from the sensing front and 512. Thisallows for trade-offs between the available storage space and theavailability of past information for storing.

Additionally, in some embodiments, the recording controller 3302 can beconfigured to store all or a portion of the digital blocks stored withinthe cache window into a file when the event detector 3300 indicates thata seizure or other event has occurred. In some embodiments, therecording controller 3302 can be configured to store all of the digitalblocks held in the window cache in a file when the event detector 3300indicates the seizure has begun or occurred and to continue to adddigital blocks to the file for an amount of time after the eventdetector 3300 indicates the seizure has been detected. In someembodiments, the recording controller 3302 can be configured to continuerecording the output from the sensing front and 512 until the eventdetector 3300 indicates that the seizure has ended. Further, in someembodiments, the recording controller 3300 can be configured to continueto add digital blocks to the file for a predetermined time after theevent detector 3300 indicates that the seizure has ended.

Optionally, the recording controller 3302 can be configured to save onlya number of digital blocks within the window, at the time detentdetector 3300 indicates the seizure has begun, corresponding to apredetermined time before the event detector 3300 indicates the seizurehas begun. This predetermined time can be any predetermined time. Forexample, but without limitation, this predetermined time can be equal to10 seconds, 30 seconds, 60 seconds, or any predetermined amount of time.Additionally, in some embodiments, the recording controller 3302 can beconfigured to allow this predetermined time to be adjusted by apractitioner.

After the recording controller 3302 has collected all the digital blockssurrounding the detection of a seizure by the event detector 3300 and toinclude the digital blocks corresponding to the predetermined timeperiods before and after the event detector 3300 indicates a seizure hasoccurred, the recording controller 3302 can save the file includingthese blocks into the memory device 516. Additionally, the recordingcontroller 3302 can stamp the file with the date and time for furtheranalysis by a practitioner. As such, the waveform analyzer 514′ providesadditional advantages in the ability to more simply distinguish betweennormal brain activity and brain activity associated with the seizure andto save the relevant portions of the brainwave signals received from thesensing front and 512 in a more efficient manner thereby saving memoryand reducing power consumption.

The above described method for generating files of selected portions ofdetected brainwave activity can be incorporated into any of the otherrecording devices 110, 110′, 110″ described above or below.

Such a method for storing selected portions of detected brainwaveactivity can also aid in the process of calibrating the amplifier 610.For example, after the initial installation of a recording device, suchas any of the recording devices 110, 110′, 110″, 110″′, and preferablyafter the patient has suffered one or more seizures, the filescontaining the selected portions of detective brainwave activity can bereviewed by a practitioner. An ordinary practitioner can readilyidentify whether or not these files of selected brainwave activityinclude brainwave activity resulting from a seizure.

If the practitioner determines that the files do not contain brainwavesresulting from seizure activity, the practitioner can use the recordingsto determine what adjustments to make to the recording device 110. Forexample, the practitioner may determine that it is necessary to adjustthe gain of the amplifier 610, or to make other adjustments.

In some embodiments, any of the recording devices 110, 110′, 110″, caninclude an alarm unit 3304 configured to provide a tactile stimulus tothe patient in which the recording device is installed. For example, thealarm unit 3304 can include a speaker, vibrator, bone conductionspeaker, or any other device that can provide a tactile stimulus to thepatient. As such, this provides an additional advantage that the patientcan be made aware that the recording device should be checked bypractitioner.

For example, the power supply 432 (FIG. 4) might need to be replaced.The memory device 516 might be full and thus not able to store or anyfurther information regarding detected seizures.

However, the alarm unit 3304 can be used to communicate with the patient112 for any reason. In an exemplary but nonlimiting embodiment, thealarm unit 3304 can emit audible beeps to the patient 112 if therecording device 110 should be checked by practitioner. For example, thealarm unit 3304 can be configured to emit 1 beep periodically toindicate that the patient 112 should contact their practitioner at theirearliest convenience. Additionally, the alarm unit 3304 can beconfigured to emit two beeps periodically to indicate that the patient112 should visit their practitioner as soon as possible. Othercommunication schemes can also be used.

An implantable version of a system according to some embodimentsadvantageously has a long-term average current consumption significantlyless than 10 microamps, allowing the implanted device to operate onpower provided by a coin cell or similarly small battery for a period ofyears without need for replacement. It should be noted, however, that asbattery and power supply configurations vary, the long-term averagecurrent consumption of a device according to some embodiments may alsovary and still provide satisfactory performance.

It should be observed that while the foregoing detailed description ofvarious embodiments of the present inventions is set forth in somedetail, the present inventions are not limited to those details and animplantable recording device 110 or neurological disorder detectiondevice made according to the inventions can differ from the disclosedembodiments in numerous ways. In particular, it will be appreciated thatembodiments of the present inventions may be employed in many differentapplications to detect anomalous neurological characteristics in atleast one portion of a patient's brain. It will be appreciated that thefunctions disclosed herein as being performed by hardware and software,respectively, may be performed differently in an alternative embodiment.It should be further noted that functional distinctions are made abovefor purposes of explanation and clarity; structural distinctions in asystem or method according to the inventions may not be drawn along thesame boundaries. Hence, the appropriate scope hereof is deemed to be inaccordance with the claims as set forth below.

1. An implantable recording device for detecting and loggingneurological events in a human patient comprising: a sensor adapted tosense neurological activity through the cranium of the patient, anelectronic module adapted to detect neurological events in the sensedneurological activity; a lead having a distal end coupled to the sensorand a proximal end coupled to the electronic module, the lead adapted tocommunicate the sensed neurological activity to the electronic module; ahousing enclosing the electronic module and into which the proximal endof the lead is inserted; and a cushioning member surrounding theelectronic module except for the portion of the housing into which theproximal end of the lead is inserted, the cushioning member being formedfrom a soft biocompatible material.
 2. The implantable recording deviceof claim 1 wherein the device is adapted to be implanted between thescalp and the cranium.
 3. The implantable recording device of claim 1wherein the device is adapted to be implanted between the scalp and thedura.
 4. An implantable device for detecting and recording neurologicalevents in a human patient comprising: a housing partially enclosing atleast one sensor coupled to an electronic module and fully enclosing theelectronic module configured to process sensed neurological activity, toidentify certain predetermined neurological activity as detected events,and to record the detected events and; a cushioning member surroundingthe electronic module and all but one surface of each of the at leastone sensor such that each sensor surface not surrounded by thecushioning member can be used to actively sense neurological activity.5. The implantable device of claim 4 wherein the at least one sensorcomprises a first sensor coupled to the electronic module and a secondsensor coupled to the electronic module, and the exposed surface of thefirst sensor is spaced apart from the exposed surface of the secondsensor such that the exposed surfaces are at approximately opposite endsof the housing.
 6. The implantable device of claim 4 wherein the atleast one sensor is coupled to the electronic module via a lead.
 7. Theimplantable device of claim 6 wherein the lead is associated with aplurality of sensors.
 8. The implantable device of claim 4 wherein theimplantable device is adapted to be implanted between the patient'sscalp and the patient's cranium, such that the exposed surface of eachof the at least one sensor is exposed to the patient's cranium.
 9. Theimplantable device of claim 4 wherein the implantable device is adaptedto be implanted between the patient's scalp and the patient's dura, suchthat the exposed surface of each of the at least one sensor is exposedto the patient's dura.
 10. An implantable device for detecting andrecording neurological events, the device comprising: a housing fullyenclosing an electronic module configured to process sensed neurologicalactivity, to identify certain predetermined neurological activity asdetected events, and to record the detected events, the housingincluding at least two connectors available for connecting leads to theelectronic module; a first sensor coupled to one of the at least twoconnectors; a second sensor coupled to one of the at least twoconnectors; and a cushioning member in which the housing, the at leasttwo connectors and the first and second sensors are suspended.
 11. Theimplantable device of claim 10 wherein the cushioning member fullysurrounds the housing, the at least two connectors, and the first andsecond sensors.
 12. The implantable device of claim 10 wherein the firstsensor and the second sensor are coupled to the same at least oneconnector via a lead.
 13. The implantable device of claim 12 wherein atleast one surface of each of the first sensor and the second sensor isexposed so that each exposed surface can be situated adjacent a bodysurface of the patient.
 14. The implantable device of claim 13 whereinthe body surface is one of the patient's cranium, galea or dura.
 15. Theimplantable device of claim 13 wherein the body surface is the patient'scranium.
 16. The implantable device of claim 10 wherein the connectorsto which the first and second sensors are coupled are different.
 17. Theimplantable device of claim 16 wherein the first sensor is coupled toone of the at least one connectors via a first lead and the secondsensor is coupled to another of the at least one connectors via a secondlead.
 18. An implantable recording device for detecting and loggingneurological events comprising: a sensor configured to generate anelectrical signal indicative of neurological activity; an electronicmodule coupled to the sensor configured to detect and log neurologicalevents in response to the generated electrical signal; a housingenclosing the electronic module and partially enclosing the sensorleaving a portion of the sensor exposed to receive the generatedelectrical signal; and a cushioning member including a top portion and abottom portion and partially surrounding the housing therebetween, thetop portion holding the housing spaced apart from and above the bottomportion, the cushioning member adapted to be oriented with the bottomportion adjacent the cranium of a human patient so that when theimplantable recording device is implanted, the housing is spaced apartfrom the cranium and the exposed portion of the sensor can be broughtinto contact with a body surface to sense neurological activitytherethrough the sensor.
 19. The implantable recording device of claim18 wherein the cushioning member is formed from a soft biocompatiblematerial.
 20. A method of monitoring a seizure disorder of an animalcomprising: implanting a seizure monitor between the epidermis and thecranium of the animal, the seizure monitor having a sensor configured todetect neurological activity and to generate an electrical signalindicative of the sensed neurological activity, a lead coupled to thesensor and to an electronic module, the electronic module configured todetect and log neurological events in the sensed electrical signal, ahousing enclosing at least the electronic module, and a cushioningmember in which the housing, the lead and the sensor are suspended, thecushioning member formed from a soft biocompatible material.
 21. Themethod of claim 20 wherein implanting the seizure monitor includesinserting the seizure monitor between the galea and the cranium of theanimal such that a surface of the sensor is exposed to and adjacent thecranium.